Recent Advances in Fault Detection and Analysis of Synchronous Motors: A Review

Abstract

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems.

1. Introduction

Synchronous motors represent an important class of electric drive systems, being widely used in industrial applications due to their high energy efficiency, precise speed control, and outstanding performance under dynamic conditions. These motors can be classified into various categories, based on the rotor configuration and the mechanism through which electromagnetic torque is produced.

With the transition toward Industry 4.0, the reliability of electric drives has become a strategic objective, and synchronous motors are no exception. Faults in these systems, whether in the stator, rotor, bearings, or power electronics, can lead to unexpected downtime, reduced efficiency, or even critical failures. As such, early fault detection and accurate diagnosis are essential for minimizing operational risks and maintenance costs. Traditional fault detection schemes, such as Motor Current Signature Analysis (MCSA), have been widely used for monitoring electrical parameters. However, they are increasingly being complemented or replaced by more sophisticated techniques [1].

Recent research indicates a strong trend toward data-driven approaches that leverage the increasing availability of sensor data and computational power. Machine learning algorithms, including deep learning models such as convolutional and recurrent neural networks, are being employed to detect complex fault patterns, offering greater adaptability and reduced reliance on expert knowledge. At the same time, hybrid diagnostic systems that combine model-based and data-driven methods are emerging as powerful tools for improving fault classification accuracy and robustness. Additionally, there is growing interest in cross-domain methods, such as acoustic-based diagnostics and advanced signal processing techniques originally developed for audio and vibration analysis, which offer non-invasive alternatives for condition monitoring [2].

Despite these advancements, several challenges remain. Data scarcity, labeling inconsistencies, high variability in operating conditions, and the lack of standardized datasets hinder the generalization and deployment of intelligent diagnostic systems in real-world industrial environments. Moreover, developing robust solutions that work in real time, under variable speed and load, continues to be a major area of research [3].

This review aims to address these issues by providing a comprehensive overview of fault detection and diagnosis methods applied to synchronous motors. The study covers recent developments between 2021 and 2025, integrating insights from model-based, data-driven, and hybrid approaches. A scientometric analysis using VOSviewer is also included to identify research trends and gaps in literature.

In addition to the classification of fault detection techniques, it is essential to provide a deeper understanding of the consequences that such faults may have in critical applications, such as downtime costs, safety risks, or cascading effects in coupled systems. This approach allows methods to be evaluated not only in terms of accuracy, but also in terms of their practical impact on the system.

1.1. Current Challenges and Research Gap in Existing Literature

A key challenge in current fault detection systems is the limited adaptability of existing signal processing techniques to real-world variability in operating conditions. While Motor Current Signature Analysis (MCSA) has proven effective in detecting faults such as rotor eccentricity, broken bars, and bearing defects, it suffers from limitations in noisy environments and non-stationary regimes. For example, the amplitude of fault-related spectral components can be obscured by structural harmonics or inverter-induced noise, making it difficult to distinguish between different types of modulation in transient current signals [4]. Although frequency-domain methods like FFT and cepstrum analysis remain widely used for PMSM diagnostics [5], their sensitivity to load variations and the requirement for signal stationarity restrict their robustness and scalability.

Bearing fault detection continues to pose a significant challenge, particularly under variable-speed or high-noise conditions. Traditional vibration-based approaches are sensitive and reliable but require external sensors and are not always feasible in embedded industrial environments. Moreover, previous studies have shown that vibration-induced mechanical wear can significantly influence electrical parameters such as contact resistance, reinforcing the importance of accounting for electromechanical interactions in diagnostic frameworks [6].

Studies highlight that even advanced time-frequency techniques like wavelet transforms may fall short in capturing incipient fault features buried under environmental noise [7]. Moreover, datasets used to train AI-based systems often suffer from imbalance—most data representing healthy states—leading to biased model behavior. This imbalance, along with the lack of labeled real-world data and the tendency to rely on lab-generated datasets, contributes to poor generalization and limited real-time applicability in practical settings.

Deep learning models have shown promising results in bearing and motor fault detection, offering high accuracy and feature extraction capabilities. Nevertheless, their adoption is hindered by issues such as computational cost, the need for large, labeled datasets, and their “black-box” nature, which reduces interpretability and trust in safety-critical applications [8]. Hybrid models—combining conventional signal processing with neural networks or fuzzy systems—are emerging as potential solutions, but they still lack standardization and benchmarking across motor types and fault conditions [9]. Furthermore, distributed faults, overlapping signatures, and environmental artifacts remain underexplored topics in PMSM and rotating machine diagnostics, warranting further investigation.

Table 1 summarizes the core aspects of the most relevant studies discussed in this section, highlighting for reference their research objectives, main contributions, and identified limitations in relation to current challenges in fault detection for PMSMs and rotating electrical machinery.

In conclusion, while fault diagnosis methods for PMSMs and rotating machinery have advanced considerably, gaps persist in areas such as multi-fault simultaneous detection, real-time deployment, sensor fusion integration, and explainable AI. Future research must address these limitations by developing lightweight, robust, and interpretable algorithms tailored for embedded systems and supported by comprehensive, labeled, and diverse datasets collected from realistic operating environments.

1.2. Research Objectives and Main Contributions

Contemporary research in the field of condition monitoring and fault diagnosis for electrical machines—particularly for PMSMs—is guided by the overarching objective of ensuring operational reliability, predictive maintenance, and system safety in dynamic industrial environments. Recent studies have pursued several interrelated goals: enhancing the accuracy and robustness of fault detection methods, reducing dependency on manual expertise, and developing frameworks that are both scalable and suitable for real-time implementation. Central to these efforts is the transition from traditional rule-based and signal-processing techniques to intelligent, data-driven, and hybrid methodologies.

One primary research objective has been to improve the diagnostic performance of signal-based techniques in non-stationary and noisy environments. Traditional approaches, such as those based on current or vibration signature analysis, while effective in controlled settings, often fail to generalize under variable load conditions or external disturbances. To address these issues, researchers have proposed enhanced feature extraction and selection mechanisms, leveraging both time–frequency analysis and machine learning algorithms. Deep learning models—such as convolutional and recurrent neural networks—have shown promising results in classifying complex fault patterns. These architectures can automatically learn discriminative features from raw signals, eliminating the need for manual preprocessing and enabling the detection of subtle or evolving faults.

Another key contribution has been the development of integrated diagnostic frameworks capable of not only identifying the presence of faults, but also determining their type, severity, and location. Such frameworks often combine anomaly detection, intelligent classification, and health assessment within a unified structure. Many of them are designed to operate using only data from normal operating conditions, thereby addressing the frequent lack of labeled fault data in industrial practice. Ensemble learning, optimization techniques, and hybrid model fusion have been introduced to improve model generalization and reduce false alarms, especially under unbalanced or limited datasets. Moreover, adaptive thresholding and confidence-based decision mechanisms are increasingly used to differentiate between uncertain or overlapping fault classes.

Further contribution from recent research lies in the movement toward condition-based maintenance strategies. Rather than merely detecting anomalies, new approaches aim to quantify the degradation level of machine components, offering real-time insights into the health state of motors. This enables early intervention, minimizes downtime, and reduces maintenance costs. Health indices derived from statistical models, machine learning outputs, or support vector domains are being used to assign confidence scores and trigger appropriate maintenance actions.

Despite these advances, several limitations persist, including the high computational cost of deep learning models, limited interpretability of black-box systems, and challenges in transferring models across different machine types or operating scenarios. Nonetheless, the cumulative progress made in recent years demonstrates a clear shift toward more autonomous, intelligent, and scalable diagnostic systems, with the aim of supporting predictive maintenance in increasingly complex and demanding industrial settings.

1.3. Structure of the Paper

The paper begins with an overview of synchronous motor technologies and the types of faults that commonly affect them. It then presents a methodological classification of fault diagnosis techniques—including model-based, data-driven, hybrid, and cross-domain approaches—followed by a comparative analysis evaluating their strengths and limitations. The review methodology is described in detail, including PRISMA compliance and scientometric analysis using VOSviewer. Subsequent sections discuss current research challenges, emerging trends, and the broader impact of fault diagnosis on predictive maintenance strategies. Finally, the paper concludes with a summary of insights and recommendations for future research directions.

2. Overview of Fault Types in Synchronous Motors

Synchronous motors, though known for their high reliability and performance, are still vulnerable to a wide range of faults that can compromise system efficiency, stability, and safety. These faults may originate from electrical, mechanical, or magnetic subsystems, and can evolve gradually or occur abruptly, depending on the operating conditions and external stresses. The identification and classification of such faults is a critical prerequisite for developing accurate diagnosis and predictive maintenance strategies. In particular, the complexity of fault behavior in synchronous motors is increased due to their diverse topologies (e.g., PMSM, SynRM, LSPMSM) and the varying mechanisms of torque production, which influence how faults manifest and propagate.

Common fault types in synchronous motors include stator winding insulation failures, rotor demagnetization (especially in PMSMs), eccentricity faults, broken rotor bars, bearing defects, and issues related to power electronic converters. Each of these fault modes affects the motor’s electromagnetic behavior differently and leaves distinct signatures in measurable signals such as current, voltage, vibration, or acoustic emissions. Understanding the physical origin and typical symptoms of each fault is essential for selecting or designing effective diagnostic techniques. In the following sections, we first present a classification of synchronous motors based on rotor structure and operating principles, followed by an overview of the major fault categories affecting these machines, including their causes, characteristic indicators, and impact on motor performance.

2.1. Classification of Synchronous Motors

A significant category is the Permanent Magnet Synchronous Motors (PMSMs), which use magnets embedded in the rotor to generate the required magnetic flux during operation. PMSMs are characterized by superior efficiency and high power density, making them suitable for high-performance applications such as electric vehicles and robotics [10]. Their growing adoption in critical systems has led to increased interest in ensuring fault tolerance and reliable operation under varying load and environmental conditions.

Synchronous Reluctance Motors (SynRMs) [11,12,13,14], on the other hand, generate torque by exploiting the variation in magnetic reluctance within the rotor, without requiring permanent magnets. This design feature offers economic advantages and ensures good structural robustness. An intermediate solution is the Permanent Magnet Assisted Synchronous Reluctance Motor (PMASynRM) [15,16], which combines the principles of the two previously mentioned technologies, offering a balanced compromise between performance and fault tolerance.

Another type is the Line-Start Permanent Magnet Synchronous Motor (LSPMSM) [17,18], which incorporates a rotor similar to that of an induction motor. This configuration allows the motor to start directly from the mains without the need for a frequency converter, and subsequently operate in synchronous mode. These characteristics make LSPMSMs ideal candidates for direct replacement of conventional induction motors.

PMSMs have become increasingly popular in transportation applications, including electric vehicles, railway traction, and aerospace systems, due to their key advantages such as high efficiency, wide speed regulation range, and low maintenance requirements. The outstanding capabilities of these motors, combined with their complex dynamic and nonlinear behavior, call for the application of sophisticated monitoring and control strategies to ensure reliable and continuous operation under varying conditions. In this context, fault detection through condition monitoring becomes critical for ensuring system safety and enabling the implementation of efficient predictive maintenance strategies [19,20].

2.2. Faults in Synchronous Motors

Synchronous motors, particularly Permanent Magnet Synchronous Motors (PMSMs), are increasingly used in high-performance industrial and automotive applications due to their efficiency, torque density, and controllability. However, these machines are susceptible to a variety of electrical and mechanical faults that can compromise their reliability and operational continuity. Understanding the origin, symptoms, and impact of these faults is crucial for implementing effective monitoring and diagnosis systems. The faults typically manifest in the stator windings, rotor magnets, bearings, or sensors, and their detection often requires specialized signal processing and machine learning techniques.

Table 2 presents a centralized overview of the most common faults occurring in synchronous motors. For each fault type, the table highlights its typical causes, symptomatic behavior, relevant detection signals, operational consequences, and widely adopted detection methods. This classification emphasizes the diversity of diagnostic approaches, from conventional spectral techniques (e.g., FFT, wavelet) to advanced intelligent algorithms (e.g., CNN-BiLSTM, GA-SVM). It also shows that most faults affect electrical signatures such as current and back-EMF, whereas others rely on vibration or flux signals. The information summarized in this table serves as a foundation for selecting fault-specific diagnostic strategies and developing integrated condition monitoring frameworks in subsequent sections.

3. Fault Diagnosis Techniques: A Methodological Classification

Fault diagnosis of synchronous motors is essential for reliability, reduced downtime, and extended lifetime. Diagnostic approaches can be grouped into four categories: model-based, data-driven, hybrid, and cross-domain. This review provides a structured classification, with emphasis on their application to synchronous motors.

The literature search was conducted across six major academic databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and MDPI) covering the period January 2021 to January 2025. The following search strings were used in various combinations: “synchronous motor fault diagnosis”, “permanent magnet synchronous motor predictive maintenance”, “PMSM fault detection”, and “synchronous reluctance motor fault diagnosis”. Inclusion criteria required that studies were peer-reviewed, written in English, and focused on synchronous motors with either experimental validation or high-fidelity simulation. Exclusion criteria comprised works addressing only induction motors, reports lacking methodological detail, non-English publications, and duplicate records. In total, 800 records were initially identified. After screening and full-text assessment, 111 studies met the eligibility criteria (Figure 1).

The resulting distribution across methodological categories (Figure 2—41% data-driven, 27% model-based, 18% hybrid, 14% cross-domain) reflects both technological trends and potential biases in study selection. The predominance of data-driven approaches is consistent with the broader adoption of machine learning and deep learning in engineering research, facilitated by the availability of high-performance computing and benchmark datasets. In contrast, model-based approaches remain less frequent, largely due to the difficulty of capturing nonlinearities and parameter variations in industrial environments. Hybrid strategies and cross-domain techniques, though underrepresented numerically, highlight emerging efforts toward interpretability, multimodal sensing, and non-invasive diagnostics.

The final distribution may also be influenced by selection bias. Restricting to peer-reviewed English-language articles underrepresents industrial reports. In addition, the predominance of laboratory-based experiments may favor data-driven validation while underestimating field-tested model-based solutions.

3.1. Model-Based Approaches—Theoretical Background

Model-based approaches represent one of the earliest and most established categories of fault diagnosis techniques for synchronous motors. These methods rely on mathematical or physical models that capture the nominal dynamic behavior of the machine. Faults are identified by comparing measured responses with model-predicted values, using techniques such as observers, parity equations, or parameter estimation.

Luenberger and Kalman observers remain central in this category. They enable state estimation of unmeasurable variables such as rotor flux or angle, with deviations from nominal predictions serving as fault indicators [26,27]. Analytical redundancy methods and parity equations also provide consistency checks between measured and expected values [28]. Parameter estimation methods, often implemented with Recursive Least Squares (RLS) or Extended Kalman Filters (EKFs), detect gradual changes in stator resistance, rotor inductance, or damping coefficients that may indicate insulation degradation or bearing wear [29].

Despite their strong theoretical foundation, these methods face several key limitations. First, they are highly sensitive to parameter variations caused by temperature drift, magnetic saturation, or machine aging. Even minor deviations between the real system and the assumed model can produce false alarms or missed detections. Second, the computational burden is non-negligible, particularly for EKF, MPC, or finite-element-based observers, making real-time implementation difficult in embedded systems. Third, these methods typically require accurate knowledge of the motor’s internal structure, which is often unavailable for commercial “black-box” machines [30]. Fourth, they struggle with non-linear operating regimes, transients, or multiple simultaneous faults, where residual-based indicators may overlap or lose discriminative power. Finally, the lack of standardized validation procedures and the limited use of large-scale experimental datasets restricts their industrial generalization. In critical applications such as water distribution systems, delayed or inaccurate fault detection through model-based methods can lead to major service disruptions and increased safety risks.

Recent research has attempted to mitigate these drawbacks. Robust and adaptive observers (e.g., sliding mode observers) improve resilience to parameter uncertainty and external disturbances. Adaptive estimation techniques continuously update model parameters online to track aging effects or load variations. Reduced-order models and grey-box modeling approaches have been introduced to lower computational complexity while maintaining diagnostic sensitivity. Furthermore, hybrid frameworks increasingly combine observer residuals with machine learning classifiers, aiming to enhance robustness and fault isolation accuracy.

Advanced control-based diagnostic methods integrate disturbance estimation and rejection directly into the control loop, producing residual signals that reveal incipient faults without requiring separate observer structures. In State-Filtered Disturbance Rejection Control (SFDRC), dual low-order filters approximate lumped disturbances—both smooth and abrupt—while an auxiliary compensator corrects filter bias. The resulting residual, defined as the difference between the measured state and the sum of the filtered state plus compensator output, delivers high sensitivity to early mechanical or magnetic faults in permanent-magnet motors. Recent innovations introduce adaptive filter gains based on the norm of the estimation error, further enhancing detection speed under variable-load conditions [31].

Active Disturbance Rejection Control (ADRC) takes a complementary approach by deploying an extended state observer (ESO) to reconstruct total disturbances—including parameter uncertainties and external torques—in real time. A residual signal is generated by comparing the ESO’s disturbance estimate to a moving average of its injection term, allowing swift fault isolation without explicit model inversion. Building on this foundation, Sliding-Mode ADRC (SM-ADRC) embeds a sliding-mode disturbance observer within the ESO architecture to achieve finite-time convergence of disturbance estimates and adaptive residual thresholds tied to the equivalent control effort. Zhou et al. demonstrated that a switching-gain ESO in surface-mounted PMSM drives isolates open-circuit and inter-turn short faults within a single electrical cycle at speeds up to 10 krpm [32], while in [33], the authors proposed an ADR-SMSC scheme that integrates a super-twisting sliding-mode speed controller with an ESO for surface-mounted PMSMs, yielding no-overshoot startup, smoother acceleration/deceleration, and markedly improved transient and steady-state speed tracking—all without requiring an accurate motor model.

Time-Varying H∞ Diagnostic Observers bring rigorous robustness guarantees to residual generation by minimizing the worst-case L₂ gain from disturbance to residual with gains scheduled on the estimated operating point. This framework yields residuals with quantifiable robustness margins and enables automatic threshold tuning, for instance, in high-precision servo drives suffering speed-sensor or encoder degradation [34].

Together, these methods unify adaptive disturbance estimation and residual-based fault-indication within control laws, markedly improving robustness to model uncertainties and nonlinearities. By embedding diagnostic intelligence into the controller itself, they enable fast, sensitive isolation of faults across wide operating regimes and complex dynamic environments.

In conclusion, while model-based approaches offer high interpretability and remain a cornerstone of fault diagnosis, their real-world deployment is constrained by sensitivity to uncertainty, computational load, and model availability. Ongoing advances in robust and adaptive observers, coupled with hybrid integration, suggest that model-based methods will continue to play a key role, particularly when interpretability and physics-based insights are critical for system safety.

3.2. Data-Driven Approaches (ML/DL)—Theoretical Background

As the operational complexity and variability of synchronous motors have increased, classical model-based techniques have often proven insufficient or difficult to scale under real-world conditions. In this context, data-driven approaches have gained significant attention, offering powerful alternatives for fault detection and classification. These methods do not require an explicit physical model of the motor but instead learn patterns directly from historical or real-time sensor data.

Traditional machine learning algorithms have been extensively applied to identify fault signatures in various motor parameters such as phase current, voltage, vibration, or temperature. Techniques like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests have been frequently used to classify faults such as phase imbalance, inter-turn short circuits, or mechanical bearing wear [35].

For instance, one study demonstrated the application of an SVM model to classify various levels of inter-turn short-circuit (ITSC) faults in stator windings, using features extracted from the frequency domain of stator current signals [36]. Other researchers have used Random Forests to identify early-stage faults in synchronous motor drives, achieving classification accuracies above 95% even in the presence of moderate measurement noise [37].

Deep learning methods have witnessed remarkable advancements over the past decade. One of their key features is the ability to automatically extract relevant features from raw data, thereby minimizing the need for manual signal processing or feature engineering. This represents a significant advantage in the context of motor fault detection.

CNNs have been widely used to analyze spectrograms, or wavelet transforms of stator currents, effectively treating them as image-like data. These networks have shown high accuracy in identifying faults such as inter-turn short circuits and partial demagnetization in PMSM [38]. Other architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have been applied to sequential time-series data, proving useful in real-time fault detection based on temporal patterns in electrical or mechanical signals [39].

More recently, hybrid deep learning frameworks have been proposed that combine CNN architectures with Bayesian optimization for hyperparameter tuning, significantly improving detection performance for ITSC faults in PMSMs. A representative study presented a Residual CNN model, whose architecture was optimized using Bayesian techniques, achieving superior estimation accuracy of fault severity and location under various load and noise conditions [40].

The data-driven approaches discussed above are highly promising due to their capabilities and scalability to generalize under varying operating conditions. However, their performance is strongly dependent on the quality, quantity, and diversity of the training data. On the other hand, deep learning models are often perceived as black-box systems, which limits their interpretability, an important drawback in industrial environments where transparency and safety assurance are critical [41]. In aerospace systems, the failure to promptly identify faults using data-driven approaches may result in cascading failures that compromise operational safety.

Data-driven fault diagnosis has evolved rapidly from conventional machine-learning classifiers to sophisticated deep-learning architectures, yet our current draft only scratches the surface. To fully capture this landscape, the section must be expanded by first surveying the full spectrum of architectures: CNNs for time-frequency feature extraction; recurrent and transformer-based models for sequential motor-current and vibration signals; graph neural networks (GNNs) that explicitly model the interconnections in multi-phase and multi-sensor arrays; and unsupervised representation learning techniques such as autoencoders and contrastive learning for anomaly detection. Detailing each class’s strengths, trade-offs, and typical use-cases will orient readers to choose the right tool for their application.

Next, emerging trends that deserve deeper treatment must be discussed. Generative-model approaches—variational autoencoders and GANs—can synthesize fault scenarios and augment scarce training sets, while self-supervised and domain-adaptation methods enable models trained on one drive or operating regime to generalize to another without exhaustive re-labeling. Case studies must be highlighted where, for instance, transformer-based encoders outperformed classic CNNs at isolating inter-turn short circuits under non-stationary speeds, or where few-shot learning frameworks detected rare bearing defects with minimal examples. These concrete examples will illustrate not only “what” has been done, but “how” new data-centric strategies overcome traditional limitations.

Finally, the crucial role of physics-informed and hybrid models must be integrated. Purely black-box networks often lack interpretability and can violate known constraints of electromagnetic or mechanical dynamics. Recent advances in embedding physical laws into loss functions, co-training neural networks alongside first-principles simulators, and fusing multi-modal inputs (current, vibration, thermal) through attention mechanisms. The proliferation of open-source motor-fault datasets and real-time, edge-computing implementations must be discussed, emphasizing benchmarks and performance metrics. By broadening the section in these three dimensions—architecture diversity, cutting-edge learning paradigms, and physics–data fusion—the reviewer’s call for expansion will be addressed and readers will be given a comprehensive toolkit for ML/DL-based diagnostics.

Drawbacks of Data-Driven Methods

Deep learning and machine-learning models have revolutionized fault diagnosis in electric drives, but they come with notable limitations. These methods typically act as black boxes, offering minimal insight into why a particular fault decision was made, which hampers trust and hinders root-cause analysis. They also exhibit “data hunger,” requiring large, labeled datasets that are often costly or impractical to collect across all motor types and fault modes. When a trained model encounters domain shifts—such as new loads, temperatures, or component aging—its diagnostic performance can degrade sharply. Finally, the computational demands for training and running these complex models pose deployment challenges on resource-constrained edge and embedded platforms.

To reconcile the power of data-driven methods with practical deployment needs, researchers have developed several countermeasures:

Explainable AI (XAI):

-. LIME and SHAP provide local feature attributions, revealing which sensor channels or time intervals most influenced each fault prediction [42,43].

-. Attention mechanisms and saliency maps embedded in CNNs visualize critical spectral or temporal signatures, making deep models more transparent.

Federated and Transfer Learning:

-. Federated learning enables collaborative model training across multiple sites without sharing raw data, preserving privacy while enriching training diversity [44].

-. Transfer learning adapts pre-trained networks from one motor family to another under scarce-label regimes, slashing annotation costs and accelerating deployment [45].

Lightweight Architectures:

-. Techniques such as model pruning, weight quantization, and knowledge distillation produce compact networks that meet strict latency and memory budgets on embedded drives [46].

Unsupervised and Self-Supervised Learning [47,48]:

-. Autoencoder-based schemes learn nominal motor behavior without fault labels, flagging anomalies via reconstruction errors.

-. Contrastive learning frameworks build robust feature embeddings that better generalize across unseen operating conditions.

3.3. Hybrid Strategies and Sensor Fusion

As discussed in the previous subsections, each approach, whether model-based or data-driven, comes with its own set of advantages and limitations. In this context, hybrid fault diagnosis strategies have emerged as a highly effective and increasingly popular solution. These methods aim to combine the interpretability and physical insight offered by model-based techniques with the adaptability and predictive power of data-driven approaches. In parallel, sensor fusion techniques have also gained significant traction, with the primary goal of enhancing the robustness and reliability of fault diagnosis by integrating information from multiple heterogeneous sensors. In the context of synchronous motors, hybrid methods are particularly beneficial, given the diversity of fault types—electrical, magnetic, and mechanical—as well as the complex interactions between them. For example, a fault in the stator windings may manifest not only in the electrical current signature but also in acoustic or vibrational patterns. Isolating such faults with high accuracy often requires leveraging multiple sources of information and diverse analytical tools.

One common hybrid strategy involves using model-based residual generation combined with machine learning classification. Here, residual quantitative differences between measured and estimated signals are computed using a motor model, for example, using observers or parity equations. These residuals are then used as input features to an ML classifier such as a Support Vector Machine or Neural Network [49]. This approach retains the diagnostic interpretability of model-based systems while improving classification performance in the presence of noise or parameter uncertainties.

Another direction in hybrid fault diagnosis includes digital twins (DTs) augmented with data-driven fault prediction modules. Digital twins provide a real-time virtual representation of the synchronous motor based on physics-based modeling. By integrating ML algorithms trained on historical sensor data, the digital twin can adapt to real-time measurements and detect anomalies or evolving faults even when the original model is imperfect [50].

Sensor fusion primarily aims to improve the accuracy, and enhance the resilience, of fault detection systems by combining data from different sensing modalities. These may include various types of sensors that collect and accumulate data such as temperature, vibration (accelerometers), acoustic signals (microphones), or electrical measurements. This approach has proven to be highly effective in detecting complex faults or early-stage defects that may not be observable within a single signal domain. Consequently, early fault detection through sensor fusion can lead to significant savings in material and maintenance resources.

When it comes to fusion, it can be applied at various levels. The first level is data-level fusion, where raw signals from multiple sensors are combined before feature extraction. The second level is feature-level fusion, in which features extracted from different sensors—such as statistical, frequency, or wavelet features—are concatenated into a common feature vector. The final type is decision-level fusion, where the outputs of individual classifiers or detectors are combined using techniques such as fuzzy logic or Bayesian inference. Advanced methods, including Kalman filtering, Bayesian networks, and Dempster–Shafer theory, have also been employed to handle uncertainty and weigh the reliability of different sensor inputs [51].

Despite their advantages, hybrid and sensor fusion systems present several challenges. These include the need for careful synchronization and calibration of heterogeneous data sources, increased system complexity, and higher computational demands. Moreover, designing effective fusion strategies often requires domain expertise to select relevant features and choose the appropriate level of integration. Nevertheless, with the advent of edge computing, the Internet of Things (IoT), and real-time digital twins, hybrid and sensor fusion-based fault diagnosis in synchronous motors is poised to become a cornerstone of intelligent condition monitoring and predictive maintenance strategies. While hybrid methods improve detection accuracy, their complexity can hinder timely intervention in industrial plants, where delayed response may escalate equipment downtime and production losses.

3.4. Cross-Domain Approaches: Adapted Acoustic Techniques for Motor Fault Diagnosis

In recent years, an increasing number of fault diagnosis frameworks have started to integrate concepts and techniques traditionally developed in other scientific domains, such as acoustics, radar, biomedical signal processing, and telecommunications. These cross-domain approaches offer new perspectives for interpreting fault signatures, especially in complex electromechanical systems like synchronous motors. Among these, acoustics-inspired methods have demonstrated promising capabilities in fault detection and localization. Synchronous motors, especially in high-performance applications, often emit characteristic acoustic or vibrational signals during operation. Deviations from normal acoustic patterns can provide early indications of mechanical imbalances, rotor–stator misalignments, bearing wear, or airgap eccentricities.

Delay-and-Sum Beamforming (DSB) is one of the most fundamental beamforming techniques originally used in microphone arrays for sound source localization. It operates by aligning and summing the time-delayed signals from multiple microphones, effectively enhancing signals from a particular direction while suppressing others. In the context of synchronous motor diagnostics, DSB has been adapted to acoustic sensor arrays placed around the motor casing. By forming directional acoustic images, this method enables the localization of fault-induced noise sources, such as partial discharges, bearing clicks, or rotor–stator contact points [52]. This approach is non-invasive, real-time, and especially useful in scenarios where direct electrical measurements are impractical or where access to internal motor components is limited.

CD-MVDR is an advanced adaptive beamforming technique initially proposed for direction-of-arrival (DoA) estimation in underwater and radar systems. It constructs a spatial filter that minimizes output power while preserving gain in the target direction, and it has superior performance in environments with correlated noise or interference. Recent research has shown that CD-MVDR can be employed to enhance the signal-to-noise ratio (SNR) of weak fault-induced vibrations or acoustic emissions in noisy industrial settings. When applied to synchronous motors, CD-MVDR-based preprocessing has enabled the detection of early-stage faults that would otherwise be masked by ambient mechanical noise or electrical interference [53]. This technique is particularly advantageous for detecting subtle and incipient mechanical faults that produce low-energy acoustic signatures, such as early-stage bearing degradation or light rotor unbalance.

The adaptation of techniques from unrelated domains fosters methodological innovation in motor fault diagnostics. For instance, time–frequency representations originally developed for EEG signal analysis have inspired the use of wavelet scalograms in motor vibration signal processing. Similarly, sparse representation methods—initially designed for image compression—are now employed to isolate fault-relevant components in current signals. Moreover, graph-based models from network theory are increasingly applied to capture interactions between components in complex electromechanical systems. Al- though these cross-domain techniques are still relatively new in the field of synchronous motor fault diagnosis, they exhibit significant potential, particularly when integrated with machine learning approaches for classification or anomaly detection.

Despite their advantages, cross-domain approaches face several significant challenges. First, identifying common ground across diverse domains is not a trivial task, as the methods must be carefully adapted to account for the physical and operational differences between the source and target domains. Second, the computational cost can be substantial—particularly for techniques such as CD-MVDR—when deployed in real-time applications. Lastly, the lack of large, standardized acoustic datasets for synchronous motors limits comparative evaluation and model validation [54]. Nevertheless, as industrial systems become increasingly instrumented and capable of multimodal sensing, these innovative approaches are expected to gain traction, especially within intelligent maintenance frameworks based on edge computing and digital twins. Adapting diagnostic techniques from acoustics or vibration analysis can be effective, but insufficient calibration under real operating conditions may lead to misdiagnosis with costly maintenance implications.

3.5. Comparative Analysis and Summary of Diagnostic Techniques

This chapter has presented a structured classification of fault diagnosis techniques for synchronous motors, offering a comprehensive overview of the methodological landscape. Each class of approaches brings distinct advantages and challenges. Selecting an appropriate method depends on application requirements, available sensors, and system complexity.

Model-based approaches provide strong physical interpretability and are well-suited for systems with well-defined mathematical models. However, their performance may degrade in the presence of parameter uncertainties or model mismatches, especially under varying operational conditions. In contrast, data-driven techniques, particularly those based on machine learning and deep learning, offer high adaptability and strong performance in learning complex, nonlinear fault patterns from sensor data. Their limitations include dependence on labeled datasets, high computational cost, and limited interpretability in critical safety applications.

Hybrid strategies aim to combine the best of both worlds, leveraging the robustness of physical modeling and the learning capability of data-driven methods. When integrated with sensor fusion, these approaches can provide enhanced fault detection coverage, especially for multimodal and incipient faults.

Finally, cross-domain approaches, such as those adapted from acoustics (e.g., DSB and CD-MVDR), offer innovative tools for fault localization and diagnosis, particularly when conventional signals (e.g., current or vibration) provide insufficient insight. These methods show promise for the future of intelligent condition monitoring, especially when integrated with real-time diagnostics and digital twins.

Table 3 provides a comparative analysis of the key characteristics and the overall impact of the main fault diagnosis approaches.

In summary, no single technique offers a universal solution. Instead, the future of synchronous motor fault diagnosis likely lies in the synergistic integration of complementary methods, supported by real-time computation, high-fidelity sensing, and explainable AI. In practice, fault detection and fault-tolerant control (FTC) are not independent stages. Diagnostic algorithms are often directly linked with adaptive or barrier-function-based FTC strategies, ensuring that once a fault is detected, the system can immediately implement corrective actions. Recognizing this integration highlights the practical relevance of the reviewed methodologies in safety-critical domains. A persistent limitation across all categories is the absence of a unified benchmarking framework, which makes it difficult to compare reported accuracies due to differences in datasets, operating conditions, and evaluation metrics.

4. Comparative Analysis of Methods and Technologies

Building on the systematic review conducted in previous sections, this chapter provides a comparative analysis of the main diagnostic approaches applied to PMSMs. The analysis is structured around four key categories identified in the literature: model-based, data-driven, hybrid, and cross-domain techniques. For each category, a selection of relevant works is presented, highlighting representative methods, diagnostic strategies, and their specific implementation details.

Each section synthesizes the contributions of these selected studies, emphasizing the diagnostic methods employed, the results obtained, and the practical challenges encountered. Centralized summary tables are included for each category to offer a clear comparative perspective on diagnostic accuracy, data requirements, computational complexity, scalability, and real-time applicability. Special attention is given to the behavior of these methods under dynamic operating conditions or limited data scenarios, as these factors significantly influence industrial deployment.

Beyond technical metrics, the analysis also discusses common practical limitations—such as model sensitivity, sensor noise, the need for large annotated datasets, or high computational demands—that are frequently reported in the literature. The structured comparison provided in this chapter aims to clarify the trade-offs between different diagnostic approaches and to highlight the context in which each category proves most effective. In this way, readers are offered both a synthesized overview and a set of practical benchmarks to guide future methodological choices.

4.1. Model-Based Approaches

Model-based approaches have played a foundational role in the development of diagnostic and predictive maintenance techniques for permanent magnet synchronous motors (PMSMs), especially in applications where reliability and safety are critical. These methods rely on the development of mathematical or physical models that describe the electrical and magnetic behavior of the machine under both healthy and faulty conditions. By comparing the measured signals with the expected model outputs, deviations can be detected and associated with specific fault types or severities.

In the context of PMSMs, model-based diagnosis is frequently applied to identify faults such as stator inter-turn short circuits (ITSCs), rotor demagnetization, eccentricity, or power electronics/sensor faults. The effectiveness of these techniques depends on the accuracy of the models, which must capture key phenomena such as mutual inductance variations, electromagnetic torque changes, and harmonic content in voltage and current signals. For example, Zhang et al. [55] developed an equivalent circuit model for PMSMs with inter-turn short-circuit faults, enabling online diagnosis based on zero-sequence voltage vector decomposition and PWM control voltage extraction, significantly lowering the detection threshold for slight ISCF in low-speed machines. Similarly, Zeng et al. [56] proposed a detailed mathematical model for stator tooth flux under rotor faults, allowing the extraction of fault parameters and quantitative assessment of demagnetization and eccentricity in PMSMs.

Model-based approaches are also instrumental in the diagnosis of power converter and sensor faults, where predictive mathematical models of the drive system are used to distinguish between different fault types and to locate faulty components [57]. In stator winding fault detection, the robustness of model-based indicators is essential, especially with respect to controller bandwidth or parameter variations. Huang et al. [58] introduced a new mathematical function for ITSC detection, combining fault information from both voltage and current signals to achieve robustness against controller bandwidth changes. Furthermore, advanced model-based methods utilize adaptive algorithms or residual analysis to extract features that are independent of load and speed, thus improving fault detection sensitivity and reliability [59].

Shi et al. [60] addressed fault-tolerant control in dual three-phase PMSM drives by introducing an adaptive model-based approach for both open-phase and open-switch faults. Their strategy eliminates the traditional requirement for explicit fault localization, which often increases system complexity and misdiagnosis risk. The method employs unified reference current vectors in the x–y plane, derived through vector space decomposition, allowing for smooth transitions between healthy and faulty states without additional hardware. Experimental validation demonstrated fast and robust switching performance, making this solution suitable for safety-critical applications such as electric vehicles and more-electric aircraft.

In another contribution, Zhou et al. [61] developed a rapid and robust model-based diagnosis method for open-switch faults in variable-speed PMSM systems. Utilizing a differential current observer, their approach generates diagnostic residuals and employs adaptive thresholding to ensure resilience against variations in speed, load, and parameter values. The method enables fault detection within just 2.9 ms (less than 10% of the current period) and minimizes false alarms, as confirmed by comprehensive experimental results.

For systems equipped with primary permanent-magnet linear motors and limited to only two current sensors, Wang et al. [62] proposed a model-based fault diagnosis technique that estimates synchronous currents using the motor model. By analyzing the frequency content of estimation errors, their method distinguishes between gain and zero-offset sensor faults and accurately identifies both the faulty phase and the fault type. The approach remains effective even under motor parameter variations and is supported by both simulation and experimental evidence.

Xu et al. [63] tackled the simultaneous diagnosis of IGBT open-circuit faults and incipient current sensor faults in PMSM inverter drives, a significant challenge due to the similar impact these faults have on phase current waveforms. By augmenting the inverter model and applying nonsingular coordinate transformations, the authors successfully decoupled the effects of each fault type. They implemented adaptive sliding mode observers for the resulting subsystems, processing the residuals with adaptive thresholds to enable precise fault localization, early detection, and robust performance even in scenarios with concurrent faults.

Heidari et al. [64] provided a comparative analysis of recent advances in synchronous reluctance motor (SynRM) drive systems, focusing on modeling strategies for design, control, and fault diagnosis. The review highlights the trade-offs between finite element analysis (FEA), analytical models, and parameter identification methods in terms of accuracy, computational burden, and suitability for real-time application. Analytical d–q axis models are identified as the most practical solution for model-based control and diagnostics, particularly when real-time parameter identification is required to account for magnetic saturation and temperature effects. The authors emphasize the ongoing importance of accurate yet computationally efficient models for modern industrial control and monitoring.

Attaianese et al. [65] proposed a novel model-based diagnostic algorithm for detecting and estimating DC offset faults in phase current sensors of field-oriented PMSM drives. By deriving a steady-state analytical solution of the PMSM drive model—including the field-oriented control (FOC) loop and the offset disturbance—the authors developed a simple, effective fault detection, isolation, and estimation (FDIE) algorithm that avoids the need for computationally intensive observers. The method enables accurate identification of both the faulty sensor phase and the magnitude of the offset, even in the presence of parameter variations, and can be implemented online or offline with minimal computational effort. Experimental validation demonstrated high accuracy (estimation error < 3.3%) and robustness across various operating conditions, with the proposed algorithm also suitable for compensation or self-commissioning functions.

Geng et al. [66] addressed the trade-off between post-fault performance and control complexity in dual three-phase PMSM drives under open-circuit faults (OCFs). They introduced a natural full-range minimum-loss (NFRML) control strategy based on analyzing the current relationship between torque and harmonic subspaces. Unlike conventional approaches that require complex fault diagnosis or optimization, the NFRML scheme uses simple real-time current reference adjustments derived from subspace current vector features. Experimental results showed smooth transitions from healthy to faulty operation, effective minimization of copper losses, and robust torque production across the full operational range—all without requiring explicit fault localization or significant storage/computational resources. The strategy was found to be universal for different fault scenarios and operating conditions, although some increase in torque ripple under fault was observed.

Yu et al. [67] presented a universal control scheme for dual three-phase PMSM drives with single open-phase faults, aiming for seamless transition between healthy and faulty conditions. Their approach integrates a closed-loop controller on the harmonic subspace, employing notch filters to automatically separate unwanted negative-sequence components, eliminating the need for fault diagnosis or control structure reconfiguration. The method also optimizes minimum copper loss during postfault operation by adjusting the positive-sequence harmonic current components. Experimental results confirmed torque-ripple-free performance and effective minimization of copper loss, with the same control strategy applicable to both normal and fault-tolerant scenarios. This work demonstrates that negative-sequence components are the root of torque ripple under open-phase faults, and that their mitigation is essential for robust, efficient operation.

While model-based methods achieve reliable performance under nominal conditions, their strong dependence on accurate parameter estimation makes them vulnerable to uncertainties and may lead to false alarms in real-world scenarios. Table 4 summarizes these aspects, along with the advantages and limitations reported in the literature.

Overall, model-based approaches provide a strong theoretical foundation for fault detection and have been extensively validated both in simulation and experimental setups. Their main advantages include interpretability, low sensor requirements, and the ability to detect incipient faults. However, these methods may be sensitive to parameter uncertainties and require accurate machine models, which can limit their applicability in highly variable operating conditions.

4.2. Data-Driven Approaches (ML/DL)

In recent years, data-driven approaches—especially those leveraging machine learning (ML) and deep learning (DL)—have emerged as the dominant trend for motor fault diagnosis and health monitoring in permanent magnet synchronous motors (PMSMs) and other rotating machinery. The increased complexity of modern drive systems, the ubiquity of high-frequency data acquisition, and the rapid advances in artificial intelligence have motivated a shift from traditional, model-based techniques to methods that automatically learn diagnostic knowledge from operational data [73].

Data-driven fault diagnosis methods offer several advantages. Unlike model-based approaches, which often require detailed and accurate system models that are difficult to obtain in real-world conditions, ML/DL methods can autonomously extract discriminative features from raw signals, making them robust to noise, system nonlinearities, and parameter variations [74,75]. Moreover, these approaches enable end-to-end diagnosis pipelines and can operate in challenging scenarios, such as under variable load or speed conditions, with minimal expert intervention [76,77].

Machine learning-based methods typically involve two stages: feature extraction and fault classification. Early ML approaches often relied on manual feature engineering and shallow classifiers such as support vector machines, k-nearest neighbors, or artificial neural networks, but deep learning has become prominent due to its capacity for automatic feature extraction and high accuracy in complex tasks [78]. In particular, CNNs have demonstrated outstanding performance in extracting hierarchical representations from raw motor signals, whether by direct analysis of current or speed waveforms, or by transforming time-series signals into two-dimensional “health images” for input to CNNs [79,80].

Another major development is the adoption of advanced architecture such as autoencoders, recurrent neural networks (RNNs), generative adversarial networks (GANs), and—most recently—transformer neural networks (TNNs), which further enhance fault detection, severity estimation, and robustness to domain shifts or data scarcity. Transfer learning (TL) has also become essential for tackling class imbalance and for transferring diagnostic knowledge between different machines, operating conditions, or datasets.

Despite these successes, data-driven approaches face several practical challenges, including the need for large, annotated datasets, sensitivity to unbalanced data, and the computational complexity of deep models—especially in real-time or embedded scenarios. Nevertheless, their ability to generalize across machines and fault types, adapt to new operating regimes, and reduce manual intervention makes them highly attractive for industrial applications and predictive maintenance strategies.

In the following, we present a selection of representative works covering a wide range of machine learning and deep learning techniques applied to PMSM fault diagnosis. For each, we highlight the core methodology, application context, key results, and reported limitations, followed by a summary table for rapid comparison.

Cai et al. [81] proposed a Bayesian network-based methodology for the early detection of faults in PMSMs using vibration and acoustic emission signals. Their approach incorporates advanced signal processing techniques—wavelet threshold denoising and minimum entropy deconvolution—to enhance the signal-to-noise ratio, followed by complementary ensemble empirical mode decomposition (CEEMD) for feature extraction. The Bayesian network is then trained to identify early, middle, and permanent fault stages. Experimental results showed that when acoustic emission signals are used, early fault diagnostic accuracy exceeds 90%, outperforming vibration-based detection, especially under variable loads. This highlights the robustness and practicality of the method for real-world PMSM monitoring.

Liu et al. [82] developed a machine-learning-based fault diagnosis framework for multiphase drive systems that integrate deep sparse filtering (DSF) with an adaptive secondary sampling mechanism. This method enables the network to process phase current data synchronized to the fundamental period of operation, thus improving feature consistency across variable speed conditions. A two-layer DSF extracts robust features from normalized current data, which are then classified using a softmax regression layer. Experimental validation on both five-phase induction machines and six-phase PMSMs showed high accuracy, strong robustness to speed and torque transients, and superior generalization capability across different drive types and control strategies. The use of adaptive secondary sampling improved diagnostic accuracy by more than 3% and reduced the likelihood of misdiagnosis during sudden transients.

Kao et al. [83] investigated both traditional feature engineering and deep learning for PMSM fault diagnosis. Their system compares wavelet packet transform (WPT)-based feature extraction with a deep one-dimensional convolutional neural network (1D-CNN), both applied to motor current signature analysis. Experimental results demonstrated that the 1D-CNN approach outperformed WPT, achieving a diagnostic accuracy of 98.8% for five distinct motor states, including demagnetization and bearing faults, across a wide speed range (150–3000 r/min). The deep learning method required minimal manual feature selection, was robust to noise, and proved effective for real-time monitoring, suggesting that CNN-based models are particularly suitable for end-to-end fault diagnosis in PMSMs.

Karabacak and Özmen introduced the use of Common Spatial Pattern (CSP) feature extraction, previously popular in medical signal processing, for the first time in machinery fault detection. Vibration and acoustic signals from worm gearboxes were processed using CSP, and classification was performed using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN). ANN classifiers using CSP features achieved higher accuracy than classical time/frequency features and even outperformed CNNs in the literature, with accuracies up to 99.1% for multiclass acoustic-based classification. The study demonstrates that CSP features, coupled with ANN, enable high classification accuracy and computational efficiency for condition monitoring under variable operating conditions, suggesting strong potential for broader adoption in rotating machinery diagnostics [84].

To support the development and benchmarking of AI-based FDD (fault detection and diagnosis) algorithms, Bacha et al. [85] published a comprehensive, publicly available dataset capturing normal operation and a variety of fault scenarios—including open/short-circuit and overheating faults—in inverter-driven PMSM systems. The dataset comprises synchronized measurements from current, voltage, and temperature sensors, along with derived features and thorough documentation, facilitating reproducible research and practical validation of ML/DL algorithms. The dataset was specifically designed to bridge the gap between algorithm development and real-world validation, accelerating the progress of data-driven approaches in industrial PMSM fault diagnosis.

A multilayer perceptron neural network has been successfully applied for current sensor fault detection in PMSM drives, as demonstrated by Jankowska and Dybkowski. Integrating the neural fault detector into a standard field-oriented control system, their method detects typical sensor issues—such as missing, intermittent, noisy, or gain-variable signals—much faster than conventional logic or observer-based algorithms. The design and training of the neural detector, including the use of Bayesian regularization and extensive empirical feature selection, ensures high robustness and generalizability across different speeds and dynamic states. Simulation studies show that this approach not only accelerates detection but can also localize the fault and distinguish between fault types, making it a practical candidate for real-time fault-tolerant PMSM applications [86].

Focusing on the early identification of permanent magnet demagnetization, Skowron et al. developed a CNN that operates directly on raw stator phase current measurements. Unlike classical approaches that rely on manual feature engineering and extensive signal preprocessing, this deep learning model autonomously extracts discriminative features for PM faults, even in the presence of simultaneous stator inter-turn short circuits. The system was experimentally validated on a purpose-built PMSM setup with physically induced PM and winding faults, achieving an overall detection accuracy of 95.8%, including real-time diagnosis under varying loads, speeds, and transient conditions. This CNN-based solution significantly reduces detection latency and minimizes the need for expert domain knowledge or additional sensors [87].

For the challenging task of diagnosing inter-turn short circuits with limited labeled fault data, Li et al. proposed a hybrid framework combining a conditional generative adversarial network (CGAN) and an optimized sparse autoencoder (OSAE). The CGAN augments small-sample datasets by generating realistic synthetic fault signals, while the OSAE, enhanced with a novel noise injection strategy, robustly extracts salient features for classification. Experimental evaluation reveals that this method achieves a diagnostic accuracy of 98.9% for ITSC detection, outperforming traditional ML and standard deep learning baselines in the few-shot regime. The approach is particularly advantageous for industrial applications where real-world fault samples are scarce or difficult to collect [88].

Zhang et al. introduced a federated learning framework leveraging few-shot learning, stacked sparse autoencoders (SSAEs), and Siamese neural networks for ITSC diagnosis in PMSMs. The model is collaboratively trained across multiple decentralized devices, each holding local data, thus preserving data privacy and reducing communication overhead. By using SSAEs for feature extraction and a Siamese architecture to learn similarity metrics, the system can achieve high fault classification accuracy even with severely limited or imbalanced data. The federated approach was validated both in centralized and distributed settings and demonstrated strong generalization and scalability for smart manufacturing applications [89].

For demagnetization fault diagnosis, Pietrzak and Wolkiewicz employed the short-time Fourier transform (STFT) to extract time-frequency features from the stator current signal, followed by classification using k-nearest neighbors (KNN) and multilayer perceptron (MLP) algorithms. The comparative study showed that both ML models could effectively detect demagnetization, with KNN providing similar accuracy to MLP but with significantly faster response times and lower computational demand. The authors’ analysis of input vector design and model parameters underscores the importance of robust feature extraction and simple ML architecture for real-time, interpretable PMSM condition monitoring [90].

Although data-driven techniques such as ML and DL demonstrate high detection accuracy, their black-box nature and significant computational requirements limit adoption in resource-constrained or safety-critical applications. Table 5 summarizes these findings, highlighting the advantages and limitations discussed in the literature

4.3. Hybrid Methods and Techniques

Hybrid diagnostic and prognosis approaches have emerged as a promising solution to overcome the individual limitations of purely model-based or data-driven methods, particularly for electric machines operating in complex and variable industrial environments. These methods combine physical models, statistical analysis, and artificial intelligence to improve diagnostic robustness, adaptability, and accuracy in scenarios where single-method approaches struggle with changing conditions, limited labeled data, or nonstationary faults [105,106,107].

Recent research demonstrates that hybrid frameworks can effectively exploit both domain knowledge—such as physical degradation models or analytical redundancy—and advanced data processing and machine learning techniques for feature extraction, selection, and classification. For example, Saucedo-Dorantes et al. propose a high-dimensional hybrid feature extraction methodology that merges time-domain and frequency-domain analysis with artificial intelligence-based feature selection and neural network classification, achieving robust detection of multiple and combined faults under varying loads and speeds. Similarly, hybrid model-based approaches, like the one proposed by Huang et al., utilize model predictive control (MPC) alongside mixed logical dynamic (MLD) models, leveraging the strengths of both physical modeling and real-time optimization for fast and reliable fault diagnosis in PMSM drives.

Other recent works demonstrate that integrating physics-based models with data-driven components—such as Bayesian networks, ARMA models, or deep learning—enables more accurate fault prognosis and better adaptation to unknown or evolving fault types. Furthermore, hybrid solutions have been expanded to include distributed and federated learning architectures to address data privacy and imbalance, enhancing generalization in Industrial IoT scenarios [108]. Multi-sensor data fusion also represents an important hybrid direction, combining complementary measurements (e.g., vibration and current signals) to increase diagnostic sensitivity and reliability under variable operating conditions [109].

The following section synthesizes key representative works, methodologies, and results in this area, highlighting the advantages and challenges of hybrid approaches in the fault diagnosis and prognosis of electrical machines.

Chen et al. present a hybrid anomaly detection method for synchronous machine winding faults by combining impulse frequency response analysis (IFRA)—which captures physical changes in winding condition—with the unsupervised Isolation Forest algorithm. By extracting mathematical and statistical indicators from the IFRA signatures, the approach robustly distinguishes healthy and faulty states, even when fault examples are extremely limited. The method achieves a high diagnostic accuracy (AUC > 0.99) and superior robustness compared to traditional supervised ML methods such as SVM, KNN, or neural networks, making it particularly suitable for periodic offline screening and unknown or rare faults [110].

Zhang et al. develop a fast diagnostic scheme for electrical faults in PMSM drives by integrating mathematical modeling of current prediction (model-based) with direct signal-based indicators from measured currents. The algorithm compares d–q axis currents predicted by a healthy model with those measured in real-time, enabling rapid identification of both the faulty phase and the defect type (open-phase/open-switch) within just a few switching cycles. Experimental validation demonstrates high efficiency and robustness—even during transient conditions—outperforming methods relying solely on signal analysis or physical modeling [111].

Huang et al. propose a semi-supervised hybrid approach for demagnetization fault diagnosis in PMSMs using leakage flux signals acquired via non-contact magnetic sensors. These signals are converted into two-dimensional Symmetrized Dot Pattern (SDP) images, from which relevant features are automatically extracted by a Wavelet Scattering Convolution Network (WSCN). A deep rule-based semi-supervised algorithm (SSDRB) is then used for classification. The solution achieves over 95% accuracy with very limited labeled data, is robust to noise, and is validated on real machines with both local and uniform demagnetization, outperforming classical and other semi-supervised approaches [112].

Meiwei et al. [113] address permanent magnet demagnetization diagnosis in electric vehicle motors by introducing temperature as a critical indicator in a hybrid diagnostic model. Through detailed thermal analysis—combining finite element simulations and experiments under various demagnetization scenarios—they construct a back-propagation neural network (BP-NN) that predicts demagnetization degree based on multiple input signals: temperature, current, speed, and torque. The hybrid model demonstrates high accuracy and generalization, showing that temperature is a sensitive and practical signal for real-world demagnetization diagnosis in automotive PMSMs.

Wang et al. implement a hybrid strategy for PMSM bearing fault diagnosis in industrial IIoT environments by combining edge processing (sensor fusion and data reduction on the IIoT node) with conventional signal analysis on the server. Both magnetic leakage flux and vibration signals are acquired non-invasively and pre-processed at the edge, reducing data transmission by 95% without sacrificing diagnostic accuracy. The approach is energy-efficient and well-suited for remote, battery-powered IIoT nodes, achieving performance comparable to conventional two-node systems, but with significantly reduced transmission and energy requirements [114].

A notable hybrid strategy combines numerical simulation (digital twin) with machine learning classification for online bearing fault diagnosis. The approach builds a high-fidelity simulation model (digital twin) of the bearing system, iteratively updates its parameters using real-world data via the Pearson correlation coefficient, and then introduces simulated fault data into the model. Machine learning models are trained on both measured and synthetic data to predict fault types in real time. This method increases diagnostic accuracy and can help overcome data scarcity, with results showing effective synchronous fault prediction and guidance for maintenance planning [115].

Another innovative hybrid technique addresses open-switch fault diagnosis in PMSM voltage-source inverters by blending model-based predictive current error analysis with fuzzy logic classification. Here, the diagnostic algorithm computes the error between reference and predicted stator currents, generating fault indicators. These variables are then processed by a fuzzy inference system, which can robustly identify single, multiple, and even intermittent faults. The approach eliminates the need for additional sensors and is validated to be robust across operating points and parameter variations [116].

For asynchronous motor fault diagnosis, a sophisticated hybrid neural architecture is proposed, integrating CNN, multi-layer perceptrons (MLPs), and an attention mechanism. This system fuses multi-sensor information: frequency spectra from FFT-processed current signals and time-frequency images from wavelet-transformed vibration data. The attention mechanism dynamically weights the contribution of each modality, optimizing diagnosis according to the fault context. Compared to unimodal or less-integrated models, this method achieves higher accuracy and reliability across a range of fault types and working conditions [117].

A cost-effective solution for inter-turn short-circuit fault diagnosis in PMSMs is achieved by combining physical search coils with advanced signal processing and coordinate transformation. The hybrid system uses a reduced number of search coils embedded along the stator, extracting high-frequency negative-sequence harmonics in the voltage signal as fault features. By performing two coordinate transformations and filtering, the fault indicator is made robust to both stationary and nonstationary conditions. Experimental results demonstrate high sensitivity and applicability in real-world electric vehicle PMSMs [118].

Finally, the integration of inter-turn fault diagnosis and torque ripple minimization in direct-torque-controlled PMSMs exemplifies a holistic hybrid strategy. This approach detects inter-turn faults using the zero-sequence voltage component, then immediately applies control actions—such as torque injection and adaptive flux observation—based on the detected fault’s phase and magnitude. Notably, it achieves both fault detection and mitigation without requiring detailed fault current estimation, enhancing fault tolerance and drive reliability under variable conditions [119].

Hybrid methods combine the strengths of multiple approaches, but their implementation complexity and demand for multidisciplinary expertise often increase development costs and hinder large-scale deployment.

Table 6 provides a summary of the key points of this subchapter.

4.4. Cross-Domain Approaches

Cross-domain approaches in the fault diagnosis of electric machines—especially permanent magnet synchronous motors (PMSMs)—refer to strategies that utilize information from multiple physical domains, sensor types, or measurement principles. Unlike traditional methods based solely on electrical signals (such as current or voltage), these techniques combine data and analysis from several domains—electrical, magnetic, mechanical, and sometimes thermal—at the level of feature extraction, sensor fusion, or decision-making. The main goal is to capture complementary information about the fault process, thereby improving robustness, sensitivity, and fault isolation, particularly in complex or safety-critical scenarios.

Although the number of cross-domain studies is relatively limited compared to model-based, data-driven, or hybrid approaches, recent works have shown that combining multiple physical measurements—such as stray magnetic flux, search coils, and vibration sensors—can significantly enhance the detection and localization of faults like inter-turn short circuits, demagnetization, or mass unbalance [120,121,122]. These cross-domain solutions offer increased sensitivity to incipient or partial failures and can provide fault information that is not easily accessible through a single measurement principle.

For example, non-intrusive flux-based techniques or the fusion of electrical and vibration signals have demonstrated practical value for both online and offline diagnostics, supporting improved maintenance planning and reliability for PMSM systems in demanding applications.

A robust cross-domain diagnostic approach for inter-turn short circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs) is proposed by Gurusamy et al. [123]. Their method relies on stray magnetic flux monitoring, integrating both fundamental and third harmonic components as fault signatures. Unlike traditional current-based techniques, this flux-based approach not only detects the ITSC fault but also localizes the faulty winding, regardless of fault severity or operating condition. Extensive simulation and experimental results demonstrate that using the third harmonic component as a diagnostic indicator reliably eliminates false-negative errors that can occur with fundamental-only monitoring, establishing a new standard for incipient fault detection in PMSMs.

A comprehensive review of flux-based condition monitoring is presented by Bostanci et al. [124], highlighting the cross-domain nature of magnetic field analysis for electrical machine diagnostics. The review synthesizes developments in sensor technologies—such as search coils, Hall-effect, fluxgate, and magnetoresistive sensors—and their integration with fault diagnosis for a wide range of faults, including stator winding, bearing, rotor, and eccentricity faults. The authors discuss how external stray flux signals can non-invasively provide rich diagnostic information, often complementary to, or even more robust than, current, vibration, or acoustic signals. The review also underscores that flux-based solutions facilitate fault localization and offer higher sensitivity and reliability, especially for PM machines, due to the stability of flux signatures over wide operating ranges.

In the context of eccentricity fault diagnosis, Im et al. [125] present a novel method leveraging a planar search coil (PSC) directly attached to the stator teeth of a PMSM. This approach detects both static and dynamic eccentricity by analyzing variations in the induced voltage due to airgap flux changes, a phenomenon not easily captured by conventional current analysis. The PSC is manufactured as a flexible printed circuit board, allowing easy retrofitting without modifying the motor’s structure. Signal processing algorithms are then applied to extract fault features. Experimental validation confirms that this method successfully discriminates between static and dynamic eccentricity at various operating speeds, even in cases where current-based techniques fail, making it a promising cross-domain solution for real-world applications.

In the field of thermal-based fault diagnosis, recent research has demonstrated promising advances in non-invasive detection techniques using image processing and machine learning. One such approach focuses on analyzing thermal images of commutator motors (CMs) and single-phase induction motors (SIMs) to identify electrical faults. This method involves the use of a thermal imaging camera combined with novel feature extraction algorithms—such as Differences of Arithmetic Mean with Otsu’s Method (DAMOM), DAM20HP, DAMMH, and Ignore Binarization (IB). The extracted features are subsequently classified using Nearest Neighbor (NN) and Long Short-Term Memory (LSTM) models.

The diagnostic system is particularly robust due to its ability to operate under realistic conditions, including minor vibrations during image acquisition, and to distinguish between multiple fault types like broken rotor coils, shorted stator coils, and combined winding faults. The proposed method achieved high recognition rates, with classification accuracies ranging from 95.33% to 100% for different motor conditions. Notably, it offers a cost-effective alternative by using a low-resolution FLIR E4 thermal camera while still delivering strong performance. This work positions thermographic imaging as a viable option for industrial fault diagnosis, complementing traditional vibration and current-based methods.

Cross-domain diagnostic methods adapted from acoustics or vibration analysis offer promising results, yet their dependence on extensive calibration reduces robustness under variable industrial conditions.

Table 7 provides a summary of the key points of this subchapter.

4.5. Semi-Quantitative Synthesis of Performance Metrics

To strengthen the comparative dimension of this review and address the concern regarding the lack of quantitative analysis, we compiled a semi-quantitative synthesis of performance metrics drawn from recent high-impact studies published between 2024 and 2025 (Table 8). The table below includes representative works from each of the four methodological categories analyzed in this review: model-based, data-driven (ML/DL), hybrid (including sensor fusion), and cross-domain approaches. For each category, two studies were selected based on their technical relevance, clarity of reported metrics, and contribution to fault diagnosis in synchronous motor systems.

While this synthesis aims to provide a structured comparison of key indicators such as accuracy, F1-score, detection latency, and deployment platforms, it is important to note that a full statistical meta-analysis was not feasible. This limitation stems from the heterogeneity in reporting standards across studies—particularly in model-based and cross-domain methods, where performance metrics are often qualitative or context-specific. Moreover, metrics such as F1-score and latency are more consistently reported in machine learning literature than in control-oriented or physics-based diagnostic frameworks. Nonetheless, the table offers a grounded overview of recent trends and trade-offs, helping to contextualize the strengths and limitations of each approach in practical fault detection scenarios.

5. Scientometric Analysis and Research Trends

This chapter presents an analysis of the selected studies published between 2021 and 2025, focusing on their thematic structure, term frequency, and citation relationships. The analysis aims to complement the systematic review by offering insights into the dominant research clusters, emerging concepts, and underexplored areas. Co-occurrence networks of keywords and concepts were generated using VOSviewer, enabling the identification of major research directions and their interrelations. Additionally, citation mapping and cluster visualization reveal how knowledge has been organized within this domain and help highlight both consolidated themes and innovation frontiers. The results of this analysis are discussed in the following sections.

5.1. Results of VOSviewer Analysis

Figure 3 presents the co-occurrence map generated using VOSviewer based on keywords extracted from studies retrieved via the query “permanent magnet synchronous motor fault detection” in Scopus. Visualization reveals the conceptual structure of the field and highlights key research themes and their interconnections.

At the center of the map, the terms “fault diagnosis” and “permanent magnet synchronous motor” dominate in size and centrality, indicating their foundational role in the corpus. These nodes are directly linked to a broad set of related concepts, suggesting a rich and multidisciplinary ecosystem around fault diagnosis for PMSMs.

From a methodological perspective, the map shows dense connections between “fault diagnosis”, “machine learning”, “convolutional neural network”, and “deep learning”, reflecting the widespread adoption of data-driven and AI-based techniques in recent research. Additionally, terms such as “feature extraction”, “support vector machine”, and “transfer learning” emphasize the growing sophistication of classification algorithms used in fault analysis.

Regarding fault types, the most frequently associated terms include “inter-turn short circuit”, “open-circuit fault”, “demagnetization”, and “current sensor fault”. These indicate the most critical and actively studied failure modes in PMSMs. The presence of related concepts like “model predictive control (MPC)”, “extended Kalman filter (EKF)”, and “flux observer” points toward the ongoing integration of model-based estimation techniques alongside data-driven approaches.

Interestingly, the map reveals interconnections between fault types and specific algorithms, such as the association of inter-turn faults with residual-based or model-predictive methods, and demagnetization with spectral techniques or CNN architectures. This implies a partial specialization of techniques for different categories of faults.

Peripheral but relevant terms such as “electric vehicles”, “condition monitoring”, and “predictive maintenance” suggest strong application-driven research motivations, particularly in transportation and industrial automation.

Figure 4 presents a bibliometric co-occurrence network of keywords extracted from publications on permanent magnet synchronous motor (PMSM) fault diagnosis, generated in VOSviewer. The term “permanent magnet synchronous motor” serves as the central hub, linking methodological, fault-type, and application-oriented keywords.

The red cluster, occupying the upper-left region, groups classic diagnostic and fault-mechanism terms. Key nodes include “fault diagnosis,” “fault detection,” “demagnetization,” “vibration analysis,” “inter-turn short circuit fault,” and “magnetic field.” This cluster underscores longstanding efforts to characterize intrinsic PMSM faults through signal-based and field-analysis techniques.

The green cluster, located in the lower-right, assembles system-level and control-focused concepts such as “fault diagnosis,” “synchronous motors,” “failure analysis,” “timing circuits,” “open-circuit fault,” “fault tolerance,” and “sensor fault.” Its composition reflects parallel research streams toward robust, real-time monitoring and fault-tolerant control architectures.

The blue cluster, found in the lower-left, comprises contemporary machine-learning and model-based inference methods. It features “convolutional neural network,” “learning systems,” “digital twin,” and “machine learning,” highlighting the field’s shift toward automated, data-driven diagnostic frameworks.

The yellow cluster, extending to the upper-right, relates to application contexts, with nodes like “electric traction,” “electric drive,” and “traction motor.” This grouping points to the growing importance of PMSM fault diagnosis within electric mobility and transportation systems.

Node size corresponds to keyword frequency, while edge thickness indicates co-occurrence strength. Together, these elements illustrate the interdisciplinary integration of traditional engineering analysis and modern computational techniques in PMSM diagnostic research.

Figure 5 presents a bibliometric co-occurrence network of keywords drawn from publications on machine-learning and deep-learning approaches to motor fault diagnosis. The two largest nodes—“machine learning” and “fault diagnosis”—occupy the center of the map, underscoring their pivotal roles in current research.

The green cluster spans most of the network and comprises methodological and application-oriented terms such as “induction motor,” “fault detection,” “condition monitoring,” “signal processing,” “vibration,” “bearing,” “artificial intelligence,” and “electric vehicle.” This grouping reflects the integration of classic diagnostic techniques with data-driven frameworks for motor health assessment.

The red cluster, located to the right, aggregates supervised learning and feature-engineering concepts. Key nodes include “support vector machine,” “random forest,” “feature selection,” “fault classification,” “CNN,” and “PRISM,” indicating a strong emphasis on traditional classification pipelines and handcrafted descriptor development for fault identification.

The blue cluster, positioned below and toward the right, highlights deep-learning paradigms. It contains “deep learning,” “transfer learning,” and “convolutional neural networks,” with links to “condition monitoring” and “predictive maintenance,” suggesting a trend toward end-to-end learning systems and intelligent prognostics.

The yellow cluster connects methodological labels to motor types, featuring “neural networks,” “induction motors,” and “permanent magnet synchronous motors,” thereby situating algorithmic advances within specific hardware domains.

Node size corresponds to keyword frequency, while edge thickness denotes co-occurrence strength, together illustrating the dominant themes and their interrelations.

This network underscores machine learning’s centrality in motor fault diagnosis, with distinct but interlinked branches exploring both feature-based classification and deep-learning architectures, all grounded in traditional signal-processing and application-driven contexts.

A particularly noteworthy observation (Figure 6) is the role of bridging nodes such as model predictive control (MPC) and transfer learning, which connect otherwise distinct clusters. Their central positioning and multiple cross-cluster links imply that these concepts serve as integrative frameworks, facilitating interdisciplinary convergence between traditional control strategies and modern data-driven techniques. This is further supported by the prominence of nodes like convolutional neural networks and fault-tolerant control, which signal a shift toward intelligent, adaptive systems capable of handling complex fault scenarios.

Moreover, the emergence of smaller, peripheral nodes—such as search coil and sliding mode observer—may indicate nascent research directions or niche applications that are beginning to gain traction. Their inclusion in the network suggests that while the field is dominated by well-established methods, there remains active exploration of alternative or complementary techniques.

In conclusion, this figure not only visualizes the conceptual landscape of PMSM fault diagnosis but also reveals underlying patterns of thematic clustering, methodological evolution, and interdisciplinary integration. Such insights are valuable for identifying mature areas of research, recognizing emerging trends, and guiding future investigations toward underexplored yet potentially impactful directions.

5.2. Concluding Remarks on Scientometric Trends

The scientometric analysis conducted using VOSviewer reveals the rich and multidimensional nature of current research on fault detection and diagnosis in PMSMs. Across the five visualizations, several consistent trends emerge that define both the thematic structure and the evolutionary direction of the field.

First, “fault diagnosis”, “permanent magnet synchronous motor”, and “condition monitoring” consistently appear as central nodes, confirming their foundational role in recent studies. The convergence of these terms with “machine learning”, “deep learning”, and “signal processing” across multiple maps underscores the rapid shift toward data-driven and intelligent diagnostic techniques, with CNNs, SVMs, and autoencoders being among the most prominent methods.

Second, there is a clear conceptual bifurcation between research focused on fault detection and classification, and that focused on fault-tolerant control. While these domains are interconnected, they often develop in parallel, with distinct clusters forming around terms like “fault tolerance”, “electric drives”, and “control systems”. This validates the interpretation that the two areas—although intertwined—require separate methodological attention.

Third, the analysis highlights the prominence of certain fault types, especially inter-turn short circuit, demagnetization, and open-circuit faults, indicating their criticality in practical applications and their frequent use as benchmarks for algorithm validation.

Moreover, the emergence of terms such as “predictive maintenance”, “internet of things”, “digital twin”, and “edge computing” suggests a growing interest in real-time implementation and industrial integration, aligning research with Industry 4.0 paradigms. However, the relatively weak linkage between these emerging technologies and fault-tolerant control strategies may reflect a gap in fully integrated architectures.

In summary, the scientometric evidence supports the view that the field is evolving toward hybrid, intelligent, and application-aware diagnostic frameworks, but still faces challenges in unifying modeling, detection, and control under a cohesive strategy. These findings serve as a foundation for identifying research gaps and for motivating future work toward scalable, robust, and real-time PMSM monitoring systems.

6. Discussion

6.1. Methodological Landscape and Trends

This systematic review provides a comprehensive analysis of fault diagnosis methodologies applied to synchronous motors, with a specific focus on Permanent Magnet Synchronous Motors (PMSMs) and Synchronous Reluctance Motors (SynRMs). The reviewed studies were classified into four major methodological categories—data-driven, model-based, hybrid, and cross-domain—based on their underlying principles and implementation strategies. The analysis revealed a notable predominance of data-driven approaches, which accounted for approximately 41% of the total reviewed studies. This aligns with the global research trend of leveraging machine learning (ML) and deep learning (DL) algorithms for real-time fault detection, pattern recognition, and predictive maintenance. These methods demonstrate significant potential in handling high-dimensional signals and capturing nonlinear relationships between operational variables and fault signatures.

The practical relevance of synchronous motor fault diagnosis becomes particularly evident in critical applications. For example, pump drive failures in water distribution networks may result in large-scale service disruptions and safety hazards, while faults in spacecraft control moment gyroscopes can compromise mission safety by affecting attitude control. These examples highlight the need for diagnostic methods that are not only accurate, but also reliable and interpretable under stringent operational conditions [135].

Model-based techniques, while less frequent, continue to play a critical role, particularly in systems where accurate mathematical modeling of motor behavior is feasible. These approaches are especially valuable for understanding system dynamics and generating residual signals in control-based diagnostic frameworks. Hybrid methods that combine physical modeling with ML inference are emerging as promising alternatives that aim to retain the interpretability of model-based systems while benefiting from the adaptive capabilities of AI. Cross-domain techniques, which include thermographic, acoustic, or vibration-based diagnostics, extend the diagnostic scope and allow non-intrusive monitoring of both electrical and mechanical faults. Their increasing adoption indicates an interest in multi-modal fault characterization and sensor fusion.

A major limitation of deep learning methods lies in their black-box nature, which raises interpretability and trust issues in safety-critical systems such as aerospace and nuclear applications. Emerging approaches such as Explainable AI (XAI) or hybridization with physical models represent promising directions to overcome these barriers.

Furthermore, the PRISMA flow diagram confirms the methodological rigor of the selection process. From over 800 initially identified records, 111 studies were ultimately included after thorough screening and eligibility assessment. This diverse set of sources enabled a robust categorization and allowed for meaningful cross-comparisons between different methodological paradigms.

6.2. Challenges and Gaps in Current Research

Despite the methodological diversity observed across the reviewed studies, several recurring limitations constrain the practical applicability and scientific robustness of current fault diagnosis approaches for synchronous motors.

First, a significant proportion of data-driven models are trained and validated on simulated or laboratory-generated datasets. While these controlled environments facilitate algorithm development, they often fail to capture the stochastic variability, noise, and operational disturbances inherent in industrial settings. This discrepancy raises concerns about the external validity and generalizability of such models.

Second, many studies focus narrowly on fault detection, neglecting critical downstream tasks such as fault classification, localization, and severity estimation. These omissions limit the utility of diagnostic systems for condition-based maintenance and decision support.

Third, the lack of standardized evaluation metrics and benchmarking protocols impedes meaningful comparison across studies. Performance indicators are often reported inconsistently, and dataset characteristics—such as class imbalance—are rarely addressed, leading to inflated accuracy scores and reduced model robustness.

Furthermore, the increasing adoption of deep learning techniques introduces challenges related to model transparency and interpretability. In safety-critical applications, the inability to explain diagnostic decisions undermines trust and hinders regulatory compliance. Few studies incorporate explainable AI (XAI) frameworks or uncertainty quantification, despite their relevance to industrial deployment.

Collectively, these gaps highlight the need for more rigorous validation practices, comprehensive diagnostic pipelines, and interpretable learning architectures. Addressing these challenges is essential for transitioning fault diagnosis systems from research prototypes to reliable industrial tools.

Despite these promising directions, significant challenges remain before data-driven diagnostics can achieve widespread industrial adoption. First, genuinely heterogeneous, real-world fault datasets are still scarce; most benchmarks derive from controlled lab tests, leaving models vulnerable to field variability. Second, although XAI tools improve transparency, their integration into maintenance workflows and control-loop safeguards is underdeveloped—engineers need actionable explanations, not just saliency maps. Third, even pruned and quantized models must prove their resilience to aging drifts, noisy measurements, and adversarial perturbations on constrained hardware. Finally, multi-fault scenarios (for example, simultaneous bearing wear and rotor imbalance) and cybersecurity threats against diagnostic networks remain largely unexplored. Bridging these gaps will require concerted efforts in dataset sharing, hybrid physics-data frameworks, ultra-efficient inference, and comprehensive validation under coexisting fault conditions.

6.3. Review Limitations and Scope Constraints

The review process itself also has inherent limitations. Firstly, the search was limited to peer-reviewed academic databases and articles written in English, which may have led to the exclusion of valuable industrial reports, non-English literature, or unpublished technical documentation. Secondly, due to the heterogeneity of the diagnostic approaches, motor types, and fault conditions covered, a meta-analysis could not be conducted. Consequently, this review does not provide a quantitative synthesis of effect sizes or performance scores. Instead, it offers a structured qualitative synthesis aimed at identifying dominant trends, methodological gaps, and areas requiring further investigation.

Additionally, while every effort was made to remove duplicate records and filter irrelevant studies, some level of subjective judgment was necessary during the screening and eligibility phases. This may have introduced selection bias, despite following the PRISMA 2020 guidelines for transparency and reproducibility.

6.4. Practical Implications and Future Research Directions

The findings of this review have several important implications for both industrial practice and academic research. From an industrial perspective, there is a clear shift toward intelligent diagnostic systems capable of operating in real-time with minimal sensor requirements. However, for widespread adoption, these systems must be both interpretable and reliable under diverse operating conditions. Lightweight algorithms that balance diagnostic accuracy with computational efficiency are needed for deployment in embedded or resource-constrained environments.

Model-based approaches are particularly effective in aerospace and automotive systems where physical models are well-defined and real-time control integration is critical. Data-driven methods excel in manufacturing environments with abundant sensor data and repetitive fault patterns. Hybrid techniques are well-suited for smart grid and robotics applications, where interpretability and adaptability must coexist. Cross-domain methods, such as vibration or thermographic analysis, are commonly deployed in rotating machinery and HVAC systems for non-intrusive fault detection.

Future research should prioritize standardized fault datasets for PMSMs and SynRMs. These should reflect varied load and environmental conditions to ensure robustness. There is also a need to integrate multiple fault modalities—such as electrical, thermal, acoustic, and mechanical signals—into unified diagnostic frameworks. In terms of algorithm development, explainable artificial intelligence (XAI), unsupervised learning, and transfer learning offer promising directions for building more robust and generalizable fault diagnosis systems.

Finally, hybrid and cross-domain approaches should be further explored for their potential to overcome the individual weaknesses of purely model-based or data-driven systems. The synergy between physical understanding and data-driven adaptability remains a critical path forward in the evolution of condition monitoring and predictive maintenance solutions for synchronous motors.

7. Conclusions

This review has systematically explored the most recent advancements in fault detection and diagnosis techniques for synchronous motors, with a focus on the period 2021–2025. Following a structured PRISMA-based methodology and integrating a scientometric perspective, the paper offers a comprehensive overview of the methodologies employed across different diagnostic categories, including model-based, data-driven, hybrid, and cross-domain approaches. By analyzing over 110 scientific contributions, this work identified dominant trends, key innovations, and persistent research gaps in the field.

One of the primary contributions of this review lies in the classification and comparative evaluation of fault diagnosis techniques. Model-based methods offer strong interpretability but remain sensitive to parameter uncertainties and model mismatches. In contrast, data-driven approaches, particularly those based on deep learning, provide high accuracy and flexibility, although they require extensive labeled datasets and computational resources. Hybrid strategies and sensor fusion techniques attempt to leverage the strengths of both worlds, improving fault detection robustness under complex operating conditions. Additionally, cross-domain solutions—such as acoustic beamforming and magnetic flux analysis—introduce promising non-invasive alternatives that expand the toolbox for real-time monitoring.

Another important contribution is the synthesis of current challenges, such as the difficulty in detecting early-stage or simultaneous faults, limited generalization of AI models trained on lab data, and the lack of standardized, labeled datasets. The review highlights the importance of developing explainable, real-time, and energy-efficient diagnostic systems tailored for industrial deployment. Future research is encouraged to focus on multi-fault scenarios, federated learning for privacy-preserving diagnostics, and integrated frameworks that combine digital twins, edge computing, and predictive control.

To facilitate meaningful comparison across fault diagnosis methods, future research should prioritize the development of a unified benchmarking framework. This could include standardized datasets representing diverse motor types and fault conditions, common evaluation metrics (e.g., precision, recall, fault severity index), and shared protocols for testing under variable operating environments. Such standardization would significantly improve reproducibility and accelerate the adoption of robust diagnostic solutions in industrial and safety-critical domains.

This review supports the advancement of intelligent fault diagnosis by consolidating the knowledge landscape and offering clear directions for future investigation. The insights and resources presented herein aim to facilitate the development of scalable, interpretable, and resilient diagnostic systems capable of supporting predictive maintenance strategies in modern industrial environments. This review has emphasized both the strengths and the limitations of current methodologies. The absence of a unified benchmarking framework, the separation of diagnostics from FTC, and the difficulty of implementing advanced methods in safety-critical scenarios remain pressing challenges. Future research should focus on standardized datasets, interpretable AI models, and validation in real industrial environments to ensure reliable adoption.

Author Contributions

Conceptualization, I.-S.G. and B.-V.V.; methodology, I.-S.G. and B.-V.V.; validation, I.-S.G. and B.-V.V.; formal analysis, I.-S.G., N.B., and B.-V.V.; investigation, I.-S.G.; resources, N.B. and G.-V.I.; data curation, N.B. and G.-V.I.; writing—original draft preparation, I.-S.G. and B.-V.V.; writing—review and editing, N.B. and G.-V.I.; visualization, G.-V.I.; supervision, N.B.; project administration, N.B. and G.-V.I.; funding acquisition, and N.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable for studies not involving humans or animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting this research have been made available in this paper.

Acknowledgments

Additional infrastructure was accessed by the projects 345/2021, SMIS 125119, “Increasing the research capacity of ICSI Ramnicu Vâlcea through the development of a CLOUD infrastructure connected to global information resources, 4C-ICSI”, funded from the European Regional Development Fund within the Competitiveness Operational Program.

Conflicts of Interest

Author Gabriel-Vasile Iana was employed by the company Mira Technologies Group, Bucharest, Romania. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 PRISMA flow diagram.

Figure 2 Distribution of reviewed publications by fault diagnosis method.

Figure 3 VOSviewer map using keywords “permanent magnet synchronous motor fault diagnosis”.

Figure 4 Expanded term co-occurrence map in PMSM fault detection literature.

Figure 5 Concept map based on “Machine Learning” and “Motor Fault Diagnosis” keywords.

Figure 6 High-level thematic map for PMSM fault diagnosis research.

Table 1

Summary of identified objectives, contributions and limitations.

Ref.	Research Objectives	Main Contributions	Limitations
[4]	To review and assess transient motor current signature analysis (TMCSA) for fault detection in variable-speed induction motors.	Detailed taxonomy of fault types and TMCSA methods; comparative evaluation of spectral and bispectral analysis; identification of frequency estimation techniques for non-stationary conditions.	Lack of experimental validation across diverse motor types; most techniques assume idealized operating conditions; computational complexity for real-time analysis not addressed.
[5]	To summarize current methods for fault diagnosis and fault-tolerant control (FTC) in PMSM drives and identify future research directions.	Comprehensive classification of model-based and data-driven techniques for PMSM diagnosis; highlights role of control strategies in FTC.	Lack of integration between diagnosis and control in real-time applications; absence of standardized datasets; limited experimental validation in industrial settings.
[7]	To provide a comprehensive review of state-of-the-art bearing fault detection methods for electrical machines.	Categorization of bearing fault types, sources, and detection methods; overview of vibration, current, and hybrid signal-based diagnostics.	Focus is broad and general; lacks focus on synchronous machines; limited discussion on real-time deployability and data requirements.
[8]	To critically review deep learning-based approaches for bearing fault classification in industrial machinery.	Comparison of conventional and deep learning models; identifies performance of CNN, RNN, and hybrid networks for classification tasks.	Dependence on large, labeled datasets; insufficient discussion on explainability and interpretability; industrial generalization remains limited.
[9]	To review intelligent techniques and signal-based methods used in condition monitoring and fault detection of electrical machines.	Provides a comprehensive overview of AI algorithms (e.g., ANN, SVM, PCA, fuzzy logic) used in diagnostics; analyzes major fault types and their signatures.	The review lacks detailed experimental comparisons; limited emphasis on practical real-time deployment; focuses more on theoretical capabilities than industrial constraints.

Table 2

Common faults in synchronous motors.

Fault Type	Cause	Typical Symptoms	Detection Signal	Impact on Operation	Common Detection Methods
Stator winding short-circuits	Insulation failure, overheat	Unbalanced current, temperature rise	Stator current, temperature	Torque pulsations, possible burnout	MCSA, thermal sensors, FFT, wavelet
Rotor demagnetization [21]	Aging, high temp, corrosion, mechanical	Symmetrical flux weakening	Airgap flux distortion	Reduced torque and efficiency	GA-SVM, FFT, airgap flux monitoring
Inter-turn short-circuit fault[22,23]	Local insulation breakdown	Slight imbalance in current, noise	Stator current	Early-stage degradation	CNN-BiLSTM, spectral analysis
Bearing defects	Wear, contamination, misalignment	Vibration, noise, harmonics	Vibration, current	Severe mechanical damage	Vibration analysis, envelope analysis
Eccentricity[24,25]	Shaft misalignment, wear	Harmonic patterns in current	Current, flux, resolver output variation	Noise, energy losses, reduced stability, bearing wear	Signal pattern analysis + ANN, FEM modeling, spectral analysis
Open-circuit fault	Broken wire, connector failure	Missing current phase	Stator current	Severe performance degradation	Observer-based, current residual analysis
Magnetic unbalance	Partial magnet damage, unequal field	Torque pulsation, speed oscillations	Back-EMF, flux	Motor instability	Flux estimation, field-oriented control
Voltage unbalance	Power grid fault, faulty inverter	Overheating, torque reduction	Voltage input	Stress on stator and control unit	Monitoring supply parameters

Table 3

Comparative table of fault diagnosis approaches.

Approach	Interpretability	Data Dependency	Robustness to Noise	Computational Complexity	Industrial Applicability
Model-Based	High (physics-based)	Low to Moderate (model parameters)	Moderate (sensitive to mismatch)	Low to Moderate	High (for known systems)
Data-Driven (ML/DL)	Low to Moderate (black-box models)	High (requires large datasets)	High (if trained properly)	High (especially DL models)	High (when data available)
Hybrid + Sensor Fusion	Moderate to High (combined reasoning)	Moderate to High (fusion and training)	High (through redundancy and fusion)	High (due to integration and fusion layers)	Very High (supports multiple faults)
Cross-Domain	Moderate (requires domain adaptation)	Moderate (depends on sensor type)	Moderate to High (beamforming improves SNR)	High (real-time processing requirements)	Emerging (needs further validation)

Table 4

Advantages and limitations of model-based approaches.

Ref.	Method Used	Application Context	Advantages	Limitations
[55]	Zero-sequence voltage vector decomposition and PWM control voltage extraction	Online diagnosis of slight inter-turn short-circuit fault (ISCF) in low-speed PMSM	- Significantly lowers ISCF detection limit (down to 1%) - Robust to inherent PMSM unbalance - Accurate phase identification	- Needs accurate PWM voltage extraction - Limited by cross-saturation effects (not considered) - Sensitive to inverter/control loop imperfections
[56]	Mathematical modeling of stator tooth flux with fault feature extraction using search coils	Online rotor fault diagnosis (demagnetization, eccentricity) in PMSM, using stator tooth flux sensors	- Real-time and reliable for distinguishing demagnetization and eccentricity - Quantitative assessment of fault parameters (degree, poles, ratio) - Applicable to key industries (wind, rail, EVs)	- Requires multiple search coils (added hardware) - Slight errors due to field nonlinearity - Accuracy depends on FE modeling and sensor calibration
[57]	FCS-MPC (Finite Control Set—Model Predictive Control) with model-based residual analysis for open-switch and current sensor faults	Matrix converter-based PMSM drives (airplanes, electric ships, military vehicles)	- Diagnoses both open-switch and current sensor faults without extra sensors - Accurate fault localization via combined residuals - HIL validation for robustness	- Threshold selection critical, sensitive to parameter variation - Large parameter deviations may cause false alarms - Hardware-in-the-loop validation, but real-world deployment may need adaptation
[58]	Rayleigh quotient-based fault indicator (combining 2nd harmonics in voltage and current)	Detection of inter-turn short-circuit (ITSC) in PMSMs, robust to controller bandwidth	- Robust against current controller bandwidth variation - Fast detection, including single-turn faults - Validated in both simulation and experiment	- Requires online extraction and processing of harmonics - May need lookup tables for healthy state - Limited by controller and hardware noise in real-world systems
[59]	Short-Time Adaline-based 2nd harmonic extraction from axis currents (Residual Insulation Monitoring)	Real-time ITSC diagnosis in PMSM (steady and dynamic conditions), residual insulation monitoring	- No extra sensors or massive data recording needed - Robust to speed/torque variations and parameter changes - High sensitivity to incipient fault stages	- Detection accuracy depends on sampling/A-D quality - High controller bandwidth may suppress current harmonics - May require calibration for varying machine types
[68]	Time Synchronous Averaging (TSA) of vibration signals (plus TSA-difference, TSA-regular, and wavelet decomposition)	Detection of mixed eccentricity faults in induction motors (can be extended to PMSM) under variable load	- Effective at noise reduction, no need for filtering - Faults detected under various load conditions - TSA-difference robust to false positives/negatives- Non-invasive vibration signal approach	- Requires vibration sensors and high sampling - TSA may need synchronization (speed info) - False alarms possible if load/speed highly variable - Limited to mechanical fault context
[69]	Airgap search coil flux monitoring and analysis of esc peak patterns for each pole	Online detection and classification of rotor and load faults (demagnetization, dynamic eccentricity, load unbalance) in PMSMs	- Can distinguish between partial, uniform demagnetization and eccentricity - Insensitive to load or controller variations - Detects faults where MCSA/vibration analysis fail - Low computational demand	- Requires search coil installation (invasive) - Not practical for legacy/compact motors - Not sensitive to pure load unbalance or static eccentricity - May require factory-level access to machine
[70]	Mathematical modeling and experimental validation (simulation and real PMSM); spectral analysis (FFT), phase current envelope; model-based fault indicators	Analysis of stator inter-turn short-circuits impact on PMSM with scalar and vector control	- Enables evaluation under both scalar and vector control - Model captures small fault levels (incipient ITSC) - Experimental verification with real data - Useful for model-based development and education	- Simplifying model assumptions (e.g., linearity, ideal conditions) - Scalar control more affected by faults, harder to detect in FOC - Model validation depends on motor parameters and experimental accuracy
[71]	Adaptive fault-tolerant control + data matching	Dual three-phase PMSM drives (open-phase and open-switch faults)	- No need for motor parameters - Accurate fault detection - Flexible optimization (e.g., minimal copper loss, maximum torque)	- Requires steady-state operation - Increased control complexity (PLL, current matching)
[72]	Comparative stator current-based techniques: FFT spectral analysis, current envelope, Discrete Wavelet Transform (DWT)	Early detection of inter-turn short-circuit faults in PMSMs using stator current measurements	- Methods experimentally compared and validated - Sensitivity to incipient faults - DWT effective for nonstationary signals - Practical for industrial monitoring	- FFT sensitive to noise, requires high-res DAQ - Some methods need careful tuning and high-quality measurements - Detection effectiveness depends on operating conditions and measurement setup

Table 5

Advantages and limitations of data-driven approaches.

Ref.	Method/Algorithm	Application Context	Strengths	Limitations
[91,92]	SVM and CNN (1D/2D, multi-scale feature extraction) for ITSC, demagnetization, bearing faults	Early and multi-level fault detection in PMSM (variable speed/load)	High accuracy (~99%), robust to varying conditions, minimal manual feature engineering, early/multiclass diagnosis	Large data and computational needs (CNN); SVM depends on feature selection, minor fault data may be scarce
[93]	Shallow NNs (MLP, RBF, SOM) with FFT-based features	Bearing fault detection/classification in PMSM	Simple, fast, effective for limited data, low complexity	Sensitive to parameter/feature selection, less robust for complex/noisy signals, not end-to-end
[94]	CNN on CWT images; data augmentation (FEA + ANN)	Early broken bar fault detection (IM/PMSM), digital twin/virtual scenarios	Works with few/no real faulty samples, enables digital/virtual training	Needs simulation validation, CNN resource intensive, may not generalize to unseen cases
[95]	Multi-channel CNN with data normalization and overlap sampling	Inter-turn short fault and High-Resistance Connection (HRC)	- High diagnostic accuracy (99.15%) - No manual feature extraction - Robust across speeds and loads	- Requires labeled fault data - Simulation-based dataset - May lag in real-time industrial deployment
[96]	Hybrid method: Bispectrum (BS) feature extraction + CNN classification	Stator winding inter-turn short circuit (ITSC) detection and classification in PMSMs	Very high classification accuracy (up to 99.4%); robust to load and speed variation; reduced training time and network size; only current sensors required; effective even for incipient faults	Requires BS computation; preprocessing step needed; generalization to other types of faults requires further testing
[97]	Residual dilated CNN with Bayesian Optimization (BO) for hyperparameter tuning	Early-stage ITSC detection and severity estimation in PMSM under various operating conditions	Learns from raw current signals; no manual feature engineering needed; BO automates tuning; achieves high accuracy across multiple fault severities; robust to variable conditions; only uses current measurements	Network complexity increases with depth; BO adds computational overhead during tuning; data-driven nature requires representative fault samples
[98]	1D ResNet with ConvLSTM regulator and multi-filter approach	Detecting ITSC and bearing faults (PMSM and CWRU dataset), using current and vibration signals	Generalizes to multiple fault types and signals; achieves 100% test accuracy even with small datasets; converges faster; robust to lower sampling rates; lower complexity than other deep variants	LSTM regulator increases model complexity; computational time still higher than traditional methods; may require adaptation for very noisy environments
[99]	Spectral analysis of symmetrical current components + K-Nearest Neighbors (KNN) classifier	On-line detection and classification of stator winding ITSC in PMSM	Simple hardware/software; fast training; sensitive even to a single shorted turn; robust to variable speed/load, explainable output	KNN less efficient for high-dimensional/large data; requires careful selection of features and K value
[100]	Deep feedforward neural network (DNN) using feature-engineered current signals	Detection and classification of single/double open-circuit faults in PWM-VSI (motor drives)	High diagnostic accuracy (>95%); strong generalization, effective for multiple fault types; no extra sensors required; scalable for multi-fault detection	Relies on extracted features, not end-to-end; DNN requires tuning; performance may be sensitive to data representativeness and sensor quality
[101]	Ensemble ML classifiers (Voting, KNN, Decision Tree, XGBoost, AdaBoost, Logistic Regression, SGD); uses current, voltage, speed, Hall sensor data	BLDC motor and inverter line fault detection/classification in EVs (simulation, real data, variable operating modes)	Real-time capability; high accuracy (>98% with voting classifier); covers multiple fault types; robust comparison with other ML methods	Relies on a rich dataset (simulated + measured); practical deployment may need sensor integration and computational resources
[102]	Average Current Park’s Vector (ACPV) analysis; threshold-based fault detection and localization	Six-phase FTPMSM (aerospace/industrial, including open/short-circuit and fault-tolerant modes)	No extra hardware/sensors needed; robust to speed/load variations; real-time; low computational burden	Limited to open-circuit power switch faults; relies on accuracy of current measurements; extension to other fault types may require adaptations
[103]	Wavelet CNN (WCNN) using normalized current vector trajectory as feature; DWT pooling for noise robustness	PMSM inverter open-circuit fault diagnosis (covers 22 fault types; validated in simulation and experiment, under noise and varying loads/speeds)	High accuracy with very small datasets; excellent noise immunity; robust to changing operating conditions; fast inference	Only open-circuit faults addressed; requires construction of current vector trajectory; network design (depth, pooling) impacts performance
[104]	Semi-supervised CRF-GAT (graph neural network + conditional random field); combines labeled/unlabeled data; learns label dependencies	Fault diagnosis in rotating machines (PMSM and IM); identifies status, severity, and working condition, including demagnetization, BRB, bearing, short-circuit faults	>97% accuracy with <10% labeled data; robust to limited labels; models both data and label relationships; scalable to many fault types and conditions	Complexity of graph/network construction; needs design/tuning for new datasets; interpretability may be challenging for practitioners

Table 6

Hybrid solutions—summary.

Ref.	Hybrid Solution Composition	Data and Sensors	Fault Types Addressed	Hybrid Advantage
[105]	Hybrid statistical features + AI/ML optimizer	Vibration, current	Multiple and combined faults (IM)	Diagnoses simultaneous faults, robust, real-time.
[106]	MPC + MLD model + data-driven indicators	Motor currents	Open-switch faults (PMSM)	Fast, accurate, robust to parameter variations.
[107]	Bayesian networks + ARMA time series	Multi-sensor degradation data	Prognosis, multi-stage degradation	Tracks stage changes, improves prognosis accuracy.
[108]	Federated stacking (NSAE + DAE) + PSO	Distributed, edge current data	ITSC, imbalanced IIoT data	Privacy, robust to imbalance, scalable, low comm. Cost.
[109]	Sensor fusion + ML classifier	Vibration, current (fusion)	Bearing faults (SRM, EV)	Enhanced discrimination, robust for EV, multi-sensor.
[110]	Isolation Forest + IFRA features	Vibration/IFRA	Winding anomaly (SM)	Accurate for rare/unlabeled data, robust, fast.
[111]	Model current prediction + signal analysis	Motor currents	Open-phase/open-switch (PMSM)	Rapid, phase/type localization, no extra sensors.
[112]	WSCN + SSDRB classifier	Magnetic leakage, SDP images	Demagnetization (PMSM)	Non-contact, few labels, noise-robust, high accuracy.
[113]	Data fusion + BP neural network	Temp, current, torque, speed	Demagnetization (EV PMSM)	Multi-signal, sensitive to fault, easy signals.
[114]	Edge data reduction + ML classifier	Vibration, current (edge/IIoT)	Bearing faults (PMSM)	Low bandwidth, scalable, preserves accuracy.
[115]	Digital twin simulation model + ML classifier	Vibration (measured and simulated)	Bearing faults (single, mixed)	Overcomes real data scarcity, adapts simulation model to real machine via PCC correction, improves classification and prognosis accuracy.
[116]	Predictive current error analysis + Fuzzy Logic	Open-switch faults in voltage source inverters for PMSM drives	Current sensors only; no extra hardware	Can detect both single and multiple faults, robust to parameter and load variations, and capable of identifying both permanent and intermittent faults. Simple implementation and real-time suitability.
[117]	Attention-based hybrid CNN-MLP with multi-sensor fusion	Fault diagnosis for three-phase asynchronous motors	Vibration and current signals	Simultaneous processing of different data types enables complementary diagnosis of electrical and mechanical faults; achieves >99% accuracy, high robustness, and cost-effectiveness; dynamic weighting improves performance over single-modality approaches.
[118]	Search coil-based voltage analysis + signal processing (coordinate transformations, LPF)	Inter-turn short-circuit faults in IPMSMs for EVs	Specially arranged search coils in stator	Very high sensitivity to ISCF; method amplifies high-frequency fault signatures, robust to speed/load changes, suitable for online real-time diagnosis. Reduced cost due to optimized SC placement.
[119]	Integration of inter-turn fault diagnosis and torque ripple minimization via advanced control	Inter-turn short-circuit faults in SPMSM with DTC	Current sensors, control system data	Joint approach for diagnosis and real-time control improvement; reduces torque ripple while maintaining accurate fault detection under varying loads.

Table 7

Cross-domain solutions—summary.

Ref.	Technique/Cross-Domain Approach	Application Context	Key Advantages	Limitations
[120]	Toroidal-yoke-type search coil flux analysis	PMSM demagnetization fault localization	Multi-pole, non-invasive, precise location	Needs additional coils; limited to flux faults
[121]	Non-intrusive leakage flux (search coil) + STFT	Rotor fault detection in salient-pole SM	Differentiates damper/field faults, no disassembly	Requires startup transient; sensitivity drop in steady state
[122]	Vibration analysis for unbalance	PMSM unbalance detection	Simple, highly effective for mechanical issues	Only mechanical faults; not generalizable
[123]	Stray magnetic flux monitoring (using both fundamental and third harmonic components) for detection and location of inter-turn short circuit faults in PMSM	Early-stage detection and localization of stator winding faults in PMSMs	Can detect and locate faulty winding; third harmonic analysis provides robust fault indication regardless of fault severity and operating point	Requires fluxgate sensor installation; fundamental component alone can miss faults at low speeds or with low severity
[124]	Review and comparative analysis of magnetic sensors (Hall, search coil, fluxgate, TMR, etc.) for internal and stray flux monitoring, with discussion of flux-based diagnostic techniques for various faults (stator, bearing, rotor, eccentricity, demagnetization, transformer faults, etc.)	Broad range of AC machines (induction, PMSM, synchronous machines, transformers)	Highlights versatility and advantages of flux-based methods; potential for location detection and improved reliability; many sensor options for non-invasive measurement	Some sensor types (search coil, Hall) are invasive; cost and complexity if multiple sensors required; practical implementation can be challenging for some fault types
[125]	Planar Search Coil (PSC) for direct detection of static and dynamic eccentricity in PMSMs via analysis of airgap flux linkage and inductance deviation	Eccentricity fault diagnosis in PMSM drives (static, dynamic, mixed eccentricity)	Detects and differentiates between static and dynamic eccentricity; flexible PCB sensor is easy to install and minimally invasive; robust against noise with proper signal processing	Requires sensor placement on stator teeth; less suitable for motors with very small airgaps; extra hardware compared to purely current-based methods
[126]	Infrared thermography for fault diagnosis using thermal signatures, combined with image processing and pattern recognition	Non-intrusive detection of electrical faults in commutator and induction motors	Enables remote, non-contact diagnosis; can visualize and classify multiple fault types using thermal patterns	Limited by camera resolution and sensitivity; affected by ambient conditions; less effective for incipient (early-stage) faults with low heat signature

Table 8

Comparative summary of recent fault diagnosis methods (2023–2025).

Fault Type	Dataset Type	Accuracy (%)	Detection Latency (ms)	Key Notes
ITSC (5% and 10% shorted turns, Rf = 0 Ω) [127]	Experimental (2 kW PMSM test bench, DSP28335 @150 MHz, 5 kHz switching)	>95% (robust under steady, dynamic, and load perturbations)	<1000 ms (fault diagnosis), current calculation in 100 ms	Accurate fault localization; superior to LPF-SMO; slight performance degradation at very low/zero speed
Current sensor incipient (phase-c, >5% amplitude), disconnection, drift [128]	HIL experimental setup (dSPACE + DSP TMS320F28335, PMSM)	>95% (validated by robust detection under disturbances)	36 ms (incipient), 1 ms (disconnection and drift)	Novel interval SMO with adaptive thresholds; robust to DC voltage fluctuation, inductance unbalance, and load torque variation
OC inverter faults + ITSC (10–30% shorted turns) [129]	Multisource dataset (3 phase currents + 3 line voltages), Simulink + PMSM experimental platform	100%	~1036 ms (full testing set inference time)	Lightweight 1D-LKCNN with multisource data fusion; fewer parameters and FLOPs than MobileNetV2/ShuffleNetV2; real-time capable for embedded drives
ITSC (early-stage, 1–4 Ω residual insulation resistance) [130]	Experimental test bench (IPMSM, dSPACE, 20 kHz) + FEM simulations	97.8–99% (avg 97.81%, max 98.97%), false alarm rate 0.66%	<1000 ms (online monitoring)	ResNet-18 CNN robust to controller bandwidth variation; superior to CNN, AE, RNN; effective on close fault degrees
Inverter open-circuit faults (T1, T1 and T3, T1 and T4) [131]	Experimental PMSM drive (DSP TMS320F28335, 10 kHz sampling, 400 sets)	92.85% (30-step adaptation); >89% consistently	<30 ms (per diagnostic run)	Hybrid method combining Luenberger observer residuals + Siamese ViT network; robust to load variation; effective with few-shot datasets
Inverter open/short-circuit, thermal faults (HB1–HB3) [132]	Experimental dataset (10,892 samples, multisensor fusion: currents, voltages, temps)	98.6% overall, F1 = 0.9786; per-class up to 100%	3.2 ms (GPU inference)	Transformer + Physics-Informed NN (PINN); robust across load/speed/temp variations; physics-based constraints reduce false alarms (0.43%)
Stator inter-turn short-circuit faults [133]	Experimental PMSM test rig (currents + vibration, 1000 samples, 50/50 split)	90.7% (fusion), 74.5% (vibration), 43.4% (current)	N/A (offline ML)	Gradient boosting with vibration-current fusion; AUC 95.1%, F1 = 0.878; data fusion greatly improves accuracy
ISCF, demagnetization (10%, 30%, 100%) [134]	Experimental PMSM datasets, cross-domain (multi-speed, multi-load, label noise, category inconsistency)	95–96% avg (DUDAN); CNN ~62%; DAN ~69%; UAN ~91%; CMU ~86%; DANCE ~82	~1000 ms (1 s sample window, 10 kHz)	DUDAN (denoising universal domain adaptation); robust to noisy labels and fault category inconsistency; H-score > 90%

References

1. Abid, A.; Khan, M.T.; Iqbal, J. A review on fault detection and diagnosis techniques: Basics and beyond. Artif. Intell. Rev.; 2021; 54, pp. 3649-3707. [DOI: https://dx.doi.org/10.1007/s10462-020-09934-2]

2. Lin, Q.; Zhou, X.; Hong, W.; Wang, K. A Comprehensive Survey of Multi-View Intelligent Fault Diagnosis Tailored to the Sensor, Machinery Equipment, and Industrial System Faults. J. Vib. Eng. Technol.; 2025; 13, 335. [DOI: https://dx.doi.org/10.1007/s42417-025-01913-7]

3. Krishna, M.S.R.; Seshadrinath, J. Model Based Stator Interturn Fault Analysis in Synchronous Reluctance Motor Drives. IEEE Trans. Ind. Appl.; 2024; 60, pp. 8878-8887. [DOI: https://dx.doi.org/10.1109/TIA.2024.3436468]

4. Yakhni, M.F.; Cauet, S.; Sakout, A.; Assoum, H.; Etien, E.; Rambault, L.; El-Gohary, M. Variable speed induction motors’ fault detection based on transient motor current signatures analysis: A review. Mech. Syst. Signal Process.; 2023; 184, 109737. [DOI: https://dx.doi.org/10.1016/j.ymssp.2022.109737]

5. Orlowska-Kowalska, T.; Wolkiewicz, M.; Pietrzak, P.; Skowron, M.; Ewert, P.; Tarchala, G.; Krzysztofiak, M.; Kowalski, C.T. Fault Diagnosis and Fault-Tolerant Control of PMSM Drives—State of the Art and Future Challenges. IEEE Access; 2022; 10, pp. 59979-60035. [DOI: https://dx.doi.org/10.1109/ACCESS.2022.3180153]

6. El Abdi, R.; Benjemaa, N. Mechanical Wear of Automotive Connectors During Vibration Tests. UPB Sci. Bull. Ser. C; 2009; 71, pp. 167-178.

7. Khan, M.A.; Asad, B.; Kudelina, K.; Vaimann, T.; Kallaste, A. The Bearing Faults Detection Methods for Electrical Machines—The State of the Art. Energies; 2023; 16, 296. [DOI: https://dx.doi.org/10.3390/en16010296]

8. Rehman, A.U.; Jiao, W.; Jiang, Y.; Wei, J.; Sohaib, M.; Sun, J.; Rehman, K.U.; Chi, Y. Deep learning in industrial machinery: A critical review of bearing fault classification methods. Appl. Soft Comput.; 2025; 171, 112785. [DOI: https://dx.doi.org/10.1016/j.asoc.2025.112785]

9. Kudelina, K.; Asad, B.; Vaimann, T.; Rassõlkin, A.; Kallaste, A.; Khang, H.V. Methods of Condition Monitoring and Fault Detection for Electrical Machines. Energies; 2021; 14, 7459. [DOI: https://dx.doi.org/10.3390/en14227459]

10. Sergakis, A.; Salinas, M.; Gkiolekas, N.; Gyftakis, K.N. A Review of Condition Monitoring of Permanent Magnet Synchronous Machines: Techniques, Challenges and Future Directions. Energies; 2025; 18, 1177. [DOI: https://dx.doi.org/10.3390/en18051177]

11. Du, G.; Zhang, G.; Li, H.; Hu, C. Comprehensive Comparative Study on Permanent-Magnet-Assisted Synchronous Reluctance Motors and Other Types of Motor. Appl. Sci.; 2023; 13, 8557. [DOI: https://dx.doi.org/10.3390/app13148557]

12. Krishna, M.S.R.; Seshadrinath, J. Modeling Analysis and Tolerant Capability for Stator Winding Interturn Fault in Synchronous Reluctance Motor with Series and Parallel Connected Windings. IEEE Trans. Ind. Electron.; 2025; 72, pp. 3136-3147. [DOI: https://dx.doi.org/10.1109/TIE.2024.3440479]

13. Tawfiq, K.B.; Ibrahim, M.N.; El-Kholy, E.E.; Sergeant, P. Performance Improvement of Synchronous Reluctance Machines—A Review Research. IEEE Trans. Magn.; 2021; 57, 8107811. [DOI: https://dx.doi.org/10.1109/TMAG.2021.3108634]

14. Murataliyev, M.; Degano, M.; Di Nardo, M.; Bianchi, N.; Gerada, C. Synchronous Reluctance Machines: A Comprehensive Review and Technology Comparison. Proc. IEEE; 2022; 110, pp. 382-406. [DOI: https://dx.doi.org/10.1109/JPROC.2022.3145662]

15. Bozkurt, A.; Baba, A.F.; Oner, Y. Design of Outer-Rotor Permanent-Magnet-Assisted Synchronous Reluctance Motor for Electric Vehicles. Energies; 2021; 14, 3739. [DOI: https://dx.doi.org/10.3390/en14133739]

16. López-Torres, C.; Riba, J.-R.; Garcia, A.; Romeral, L. Detection of Eccentricity Faults in Five-Phase Ferrite-PM Assisted Synchronous Reluctance Machines. Appl. Sci.; 2017; 7, 565. [DOI: https://dx.doi.org/10.3390/app7060565]

17. Kazakbaev, V.; Paramonov, A.; Dmitrievskii, V.; Prakht, V.; Goman, V. Indirect Efficiency Measurement Method for Line-Start Permanent Magnet Synchronous Motors. Mathematics; 2022; 10, 1056. [DOI: https://dx.doi.org/10.3390/math10071056]

18. Ould Lahoucine, Y.; Raute, R.; Caruana, C. Line-Start Permanent Magnet Synchronous Motors: Evolution, Challenges, and Industrial Prospects. Energies; 2025; 18, 4545. [DOI: https://dx.doi.org/10.3390/en18174545]

19. Gong, H.; Yang, Y.; Zhang, Y.; Shi, T.; Yang, Y.; Zhang, Y. Digital twin model of a permanent magnet synchronous motor via a multiphysics-informed deep operator network. IEEE Trans. Ind. Electron.; 2024; 71, pp. 5022-5032. [DOI: https://dx.doi.org/10.1016/j.ymssp.2025.112561]

20. Zhou, Z.; Wang, J.; Geng, Q. Noninvasive Prognostic and Health Management for Permanent Magnet of PMSM Drive System Through Comprehensive Parameter Identification. IEEE Trans. Transp. Electrif.; 2025; 11, pp. 10498-10510. [DOI: https://dx.doi.org/10.1109/TTE.2025.3555271]

21. Jeong, J.; Lee, H.; Orviz, M.; Lee, S.B.; Reigosa, D.; Briz, F. Detection of Trailing Edge PM Demagnetization in Surface PM Synchronous Motors. IEEE Trans. Ind. Appl.; 2023; 59, pp. 3390-3399. [DOI: https://dx.doi.org/10.1109/TIA.2023.3247402]

22. Chen, Z.; Liang, D.; Jia, S.; Yang, L.; Yang, S. Incipient Interturn Short-Circuit Fault Diagnosis of Permanent Magnet Synchronous Motors Based on the Data-Driven Digital Twin Model. IEEE J. Emerg. Sel. Top. Power Electron.; 2023; 11, pp. 3514-3524. [DOI: https://dx.doi.org/10.1109/JESTPE.2023.3255249]

23. Xiao, L.; Li, S.; Liu, C.; Zou, J. Inter-Turn Short-Circuit Fault Diagnosis Method for Dual Three-Phase PMSM Based on Voltage Demodulation. IEEE Trans. Magn.; 2025; 61, pp. 1-5. [DOI: https://dx.doi.org/10.1109/TMAG.2025.3528942]

24. Huang, F.; Chen, C.; Wang, Z.; Fu, H. Research on vibration characteristics of permanent magnet synchronous motor rotor parallel eccentricity and bearing inner ring radial misalignment coupling fault. Electr. Eng.; 2025; 107, pp. 7909-7931. [DOI: https://dx.doi.org/10.1007/s00202-025-02949-4]

25. Kang, J.-K.; Heo, J.-H.; Lee, S.-W.; Hur, J. A New Eccentricity Fault Diagnosis Method for IPMSM Using Variable Reluctance Resolver. IEEE Trans. Ind. Appl.; 2025; 61, pp. 7117-7128. [DOI: https://dx.doi.org/10.1109/TIA.2025.3561713]

26. Romdhane, M.; Naoui, M.; Mansouri, A. PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles. Electronics; 2023; 12, 3758. [DOI: https://dx.doi.org/10.3390/electronics12183758]

27. Guo, X.; Sun, W.; Jiang, D.; Qu, R. Application of the Luenberger Observer for Rotor Resistance Estimation in Induction Motor Drives. Proceedings of the 2023 IEEE 6th International Electrical and Energy Conference (CIEEC); Wuhan, China, 19 July 2023; [DOI: https://dx.doi.org/10.1109/CIEEC58067.2023.10166237]

28. Rodriguez-Blanco, M.A.; Golikov, V.; Osorio-Sánchez, R.; Samovarov, O.; Ortiz-Torres, G.; Sanchez-Lara, R.; Vazquez-Avila, J.L. Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations. Energies; 2022; 15, 8372. [DOI: https://dx.doi.org/10.3390/en15228372]

29. De Souza, D.A.; Batista, J.G.; Vasconcelos, F.J.S.; Dos Reis, L.L.N.; Machado, G.F.; Costa, J.R.; Junior, J.N.N.; Silva, J.L.N.; Rios, C.S.N.; Junior, A.B.S. Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator. IEEE Access; 2021; 9, pp. 63779-63789. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3074419]

30. Dehnavi, V.S.; Shafiee, M. Fault Diagnosis of Induction Motors Using Novel Measurement Techniques and Data Fusion. Measurement; 2025; 256, 118135. [DOI: https://dx.doi.org/10.1016/j.measurement.2025.118135]

31. Yang, G. State filtered disturbance rejection control. Nonlinear Dyn.; 2025; 113, pp. 6739-6755. [DOI: https://dx.doi.org/10.1007/s11071-024-10449-6]

32. Xu, J.; Wei, Z.; Hu, L.; Zhang, M. Active Disturbance Rejection Resonant Control with Decoupling Extended Observation Branch for Current Harmonic Suppression of DTP-PMSM. IEEE J. Emerg. Sel. Top. Power Electron.; 2025; Early Access, [DOI: https://dx.doi.org/10.1109/JESTPE.2025.3571477]

33. Zhu, Y.; Zhou, Y.; Gui, X.; Chai, J. Sliding-Mode Speed Control Based on Active-Disturbance-Rejection for Permanent-Magnet Synchronous Motors. Proceedings of the 7th International Electrical and Energy Conference (CIEEC); Beijing, China, 10–12 May 2024; pp. 724-729. [DOI: https://dx.doi.org/10.1109/CIEEC60922.2024.10583487]

34. Tahami, H.; Saberi, S.; Ali, B.M.; AbdulAmeer, S.; Abdul Hussein, A.H.; Chaoui, H. A Robust H∞-Based State Feedback Control of Permanent Magnet Synchronous Motor Drives Using Adaptive Fuzzy Sliding Mode Observers. Actuators; 2024; 13, 307. [DOI: https://dx.doi.org/10.3390/act13080307]

35. Kawady, T.A.; Khater, W.M.; Abu-Faty, H.G.; Izzularab, M.A.; Ibrahim, M.E. AI Driven Fault Diagnosis Approach for Stator Turn-to-Turn Faults in Induction Motors. Sci. Rep.; 2025; 15, 20125. [DOI: https://dx.doi.org/10.1038/s41598-025-04462-x]

36. Shih, K.-J.; Hsieh, M.-F.; Chen, B.-J.; Huang, S.-F. Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors. IEEE Trans. Magn.; 2022; 58, 8204307. [DOI: https://dx.doi.org/10.1109/TMAG.2022.3169173]

37. Kasap, M.; Çınar, E.; Yazıcı, A.; Özkan, K. An Experimental Evaluation of Intelligent Fault Detection and Classification for Induction Motors Utilizing Machine Learning Approaches. J. ESOGU Engin. Arch. Fac.; 2021; 29, pp. 126-136. [DOI: https://dx.doi.org/10.31796/ogummf.853090]

38. Mohammad-Alikhani, A.; Jamshidpour, E.; Dhale, S.; Akrami, M.; Pardhan, S.; Nahid-Mobarakeh, B. Fault Diagnosis of Electric Motors by a Channel-Wise Regulated CNN and Differential of STFT. IEEE Trans. Ind. Appl.; 2025; 61, pp. 3066-3077. [DOI: https://dx.doi.org/10.1109/TIA.2025.3532556]

39. Huang, T.; Zhang, Q.; Tang, X.; Zhao, S.; Lu, X. A Novel Fault Diagnosis Method Based on CNN and LSTM and Its Application in Fault Diagnosis for Complex Systems. Artif. Intell. Rev.; 2021; 54, pp. 1289-1315. [DOI: https://dx.doi.org/10.1007/s10462-021-09993-z]

40. Baharvand, A.H.; Beigi Fard, S.H.; Poursaeed, A.H.; Doostizadeh, M. An Optimized Classifier Chains-Based Deep Learning Framework for Inter-Turn Fault Diagnosis in Permanent Magnet Synchronous Motors. Appl. Soft Comput.; 2025; 180, 113482. [DOI: https://dx.doi.org/10.1016/j.asoc.2025.113482]

41. Presciuttini, A.; Cantini, A.; Costa, F.; Portioli-Staudacher, A. Machine Learning Applications on IoT Data in Manufacturing Operations and Their Interpretability Implications: A Systematic Literature Review. J. Manuf. Syst.; 2024; 74, pp. 477-486. [DOI: https://dx.doi.org/10.1016/j.jmsy.2024.04.012]

42. Kim, K.J.; Park, J.H.; Min, D.H.; Min, S.G. Data-Driven Optimization of Aspect Ratio in Permanent Magnet Machines Using Deep Learning and SHAP Analysis. IEEE Access; 2025; 13, pp. 122164-122176. [DOI: https://dx.doi.org/10.1109/access.2025.3586216]

43. Iwata, K.; Sasaki, H. Design-LIME: An Interpretable Visualization Method for Electric Motor Design Based on Deep Learning. IEEE Access; 2025; 13, pp. 73697-73707. [DOI: https://dx.doi.org/10.1109/ACCESS.2025.3563351]

44. Vlachou, V.I.; Karakatsanis, T.S.; Efstathiou, D.E.; Vlachou, E.I.; Vologiannidis, S.D.; Balaska, V.E.; Gasteratos, A.C. Condition Monitoring and Fault Prediction in PMSM Drives Using Machine Learning for Elevator Applications. Machines; 2025; 13, 549. [DOI: https://dx.doi.org/10.3390/machines13070549]

45. Sung, M.-H.; Park, S.-H.; Cha, K.-S.; Sim, J.-H.; Lim, M.-S. Deep Transfer Learning-Based Performance Prediction Considering 3-D Flux in Outer Rotor Interior Permanent Magnet Synchronous Motors. Machines; 2025; 13, 302. [DOI: https://dx.doi.org/10.3390/machines13040302]

46. Yu, H.; Li, S.; Xu, Y.; Zhang, Z.; Mu, H.; Han, W. Time-Frequency Domain Lightweight Dual-Branch MSCFormer for PMSM ITSC Fault Diagnosis. IEEE Trans. Ind. Electron.; 2025; Early Access, [DOI: https://dx.doi.org/10.1109/TIE.2025.3579108]

47. Lee, S.K.; Kim, H.; Chae, M.; Oh, H.J.; Yoon, H.; Youn, B.D. Self-Supervised Feature Learning for Motor Fault Diagnosis under Various Torque Conditions. Knowl.-Based Syst.; 2024; 288, 111465. [DOI: https://dx.doi.org/10.1016/j.knosys.2024.111465]

48. Wu, X.; Li, M.; Geng, Y.; Qian, H. Unsupervised Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Guided by Density-Based Clustering and Deep Learning. IEEE Trans. Instrum. Meas.; 2025; 74, 3503811. [DOI: https://dx.doi.org/10.1109/TIM.2024.3522658]

49. Drakaki, M.; Karnavas, Y.L.; Tziafettas, I.A.; Linardos, V.; Tzionas, P. Machine Learning and Deep Learning Based Methods Toward Industry 4.0 Predictive Maintenance in Induction Motors: A State of the Art Survey. J. Ind. Eng. Manag.; 2022; 15, pp. 31-57. [DOI: https://dx.doi.org/10.3926/jiem.3597]

50. Kohtz, S.; Zhao, J.; Renteria, A.; Lalwani, A.; Xu, Y.; Zhang, X.; Haran, K.S.; Senesky, D.; Wang, P. Optimal Sensor Placement for Permanent Magnet Synchronous Motor Condition Monitoring Using a Digital Twin-Assisted Fault Diagnosis Approach. Reliab. Eng. Syst. Saf.; 2024; 242, 109714. [DOI: https://dx.doi.org/10.1016/j.ress.2023.109714]

51. Weddington, J.; Niu, G.; Chen, R.; Yan, W.; Zhang, B. Lithium-Ion Battery Diagnostics and Prognostics Enhanced with Dempster-Shafer Decision Fusion. Neurocomputing; 2021; 458, pp. 440-453. [DOI: https://dx.doi.org/10.1016/j.neucom.2021.06.057]

52. Chelmiah, E.T.; Madigan, C.D.; Kavanagh, D.F. An Acoustic Sensor Array Approach for Localising Partial Discharges in Electric Machines. Mech. Syst. Signal Process.; 2024; 214, 111354. [DOI: https://dx.doi.org/10.1016/j.ymssp.2024.111354]

53. Athisayam, A.; Kondal, M. Noise-Resilient Bearing Fault Diagnosis Using DTW-Based IMF Selection and Wavelet-Fused Features with the Bat Algorithm. Signal Image Video Process.; 2025; 19, 607. [DOI: https://dx.doi.org/10.1007/s11760-025-04227-z]

54. Xie, F.; Li, G.; Fan, Q.; Xiao, Q.; Zhou, S. Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion. Processes; 2023; 11, 2862. [DOI: https://dx.doi.org/10.3390/pr11102862]

55. Zhang, Y.; Liu, G.; Zhao, W.; Zhou, H.; Chen, Q.; Wei, M. Online Diagnosis of Slight Interturn Short-Circuit Fault for a Low-Speed Permanent Magnet Synchronous Motor. IEEE Trans. Transp. Electrif.; 2021; 7, pp. 104-113. [DOI: https://dx.doi.org/10.1109/TTE.2020.2991271]

56. Zeng, C.; Huang, S.; Lei, J.; Wan, Z.; Yang, Y. Online Rotor Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Tooth Flux. IEEE Trans. Ind. Appl.; 2021; 57, pp. 2366-2377. [DOI: https://dx.doi.org/10.1109/TIA.2021.3058541]

57. Dan, H.; Yue, W.; Xiong, W.; Liu, Y.; Su, M.; Sun, Y. Open-Switch and Current Sensor Fault Diagnosis Strategy for Matrix Converter-Based PMSM Drive System. IEEE Trans. Transp. Electrific.; 2022; 8, pp. 875-885. [DOI: https://dx.doi.org/10.1109/TTE.2021.3109093]

58. Huang, S.; Aggarwal, A.; Strangas, E.G.; Li, K.; Niu, F.; Huang, X. Robust Stator Winding Fault Detection in PMSMs with Respect to Current Controller Bandwidth. IEEE Trans. Power Electron.; 2021; 36, pp. 5032-5042. [DOI: https://dx.doi.org/10.1109/TPEL.2020.3030036]

59. Wei, D.; Liu, K.; Hu, W.; Peng, X.; Chen, Y.; Ding, R. Short-Time Adaline Based Fault Feature Extraction for Inter-Turn Short Circuit Diagnosis of PMSM via Residual Insulation Monitoring. IEEE Trans. Ind. Electron.; 2023; 70, pp. 3103-3114. [DOI: https://dx.doi.org/10.1109/TIE.2022.3167164]

60. Shi, P.; Wang, X.; Meng, X.; He, M.; Mao, Y.; Wang, Z. Adaptive Fault-Tolerant Control for Open-Circuit Faults in Dual Three-Phase PMSM Drives. IEEE Trans. Power Electron.; 2023; 38, pp. 3676-3688. [DOI: https://dx.doi.org/10.1109/TPEL.2022.3223411]

61. Zhou, X.; Sun, J.; Cui, P.; Lu, Y.; Lu, M.; Yu, Y. A Fast and Robust Open-Switch Fault Diagnosis Method for Variable-Speed PMSM System. IEEE Trans. Power Electron.; 2021; 36, pp. 2598-2610. [DOI: https://dx.doi.org/10.1109/TPEL.2020.3013628]

62. Wang, W.; Tian, W.; Wang, Z.; Hua, W.; Cheng, M. A Fault Diagnosis Method for Current Sensors of Primary Permanent-Magnet Linear Motor Drives. IEEE Trans. Power Electron.; 2021; 36, pp. 2334-2345. [DOI: https://dx.doi.org/10.1109/TPEL.2020.3011125]

63. Xu, S.; Chen, X.; Liu, F.; Wang, H.; Chai, Y.; Zheng, W.X.; Chen, H. A Novel Adaptive SMO-Based Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Incipient Faults of Inverters in PMSM Drives for Electric Vehicles. IEEE Trans. Instrum. Meas.; 2023; 72, 3526915. [DOI: https://dx.doi.org/10.1109/TIM.2023.3305653]

64. Heidari, H.; Rassolkin, A.; Kallaste, A.; Vaimann, T.; Andriushchenko, E.; Belahcen, A.; Lukichev, D.V. A Review of Synchronous Reluctance Motor-Drive Advancements. Sustainability; 2021; 13, 729. [DOI: https://dx.doi.org/10.3390/su13020729]

65. Attaianese, C.; D’Arpino, M.; Di Monaco, M.; Di Noia, L.P. Model-Based Detection and Estimation of DC Offset of Phase Current Sensors for Field Oriented PMSM Drives. IEEE Trans. Ind. Electron.; 2023; 70, pp. 6316-6325. [DOI: https://dx.doi.org/10.1109/TIE.2022.3192683]

66. Geng, Q.; Li, Z.; Wang, H.; Zhang, G.; Zhou, Z. Natural Fault-Tolerant Control with Minimum Copper Loss in Full Torque Operation Range for Dual Three-Phase PMSM Under Open-Circuit Fault. IEEE Trans. Power Electron.; 2024; 39, pp. 1279-1291. [DOI: https://dx.doi.org/10.1109/TPEL.2023.3323570]

67. Yu, K.; Wang, Z.; Gu, M.; Wang, X. Universal Control Scheme of Dual Three-Phase PMSM Drives with Single Open-Phase Fault. IEEE Trans. Power Electron.; 2022; 37, pp. 14034-14039. [DOI: https://dx.doi.org/10.1109/TPEL.2022.3195526]

68. Alimardani, R.; Rahideh, A.; Hedayati Kia, S. Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals. IEEE Trans. Ind. Electron.; 2024; 71, pp. 3173-3181. [DOI: https://dx.doi.org/10.1109/TIE.2023.3266589]

69. Rafaq, M.S.; Lee, H.-J.; Park, Y.; Lee, S.B.; Zapico, M.O.; Fernandez, D.; Reigosa, D.; Briz, F. Airgap Search Coil Based Identification of PM Synchronous Motor Defects. IEEE Trans. Ind. Electron.; 2022; 69, pp. 6551-6560. [DOI: https://dx.doi.org/10.1109/TIE.2021.3095810]

70. Krzysztofiak, M.; Skowron, M.; Orlowska-Kowalska, T. Analysis of the Impact of Stator Inter-Turn Short Circuits on PMSM Drive with Scalar and Vector Control. Energies; 2021; 14, 153. [DOI: https://dx.doi.org/10.3390/en14010153]

71. Wang, X.; Jiang, H.; Shi, P.; Zhou, Y.; Xiao, D.; Wang, Z. Adaptive Fault-Tolerant Control-Based Fault Diagnosis of Dual Three-Phase PMSM Drives. IEEE J. Emerg. Sel. Top. Power Electron.; 2025; 13, pp. 2556-2564. [DOI: https://dx.doi.org/10.1109/JESTPE.2025.3528107]

72. Pietrzak, P.; Wolkiewicz, M. Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents. Energies; 2021; 14, 1630. [DOI: https://dx.doi.org/10.3390/en14061630]

73. Cen, J.; Yang, Z.; Liu, X.; Xiong, J.; Chen, H. A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms. J. Vib. Eng. Technol.; 2022; 10, pp. 2481-2507. [DOI: https://dx.doi.org/10.1007/s42417-022-00498-9]

74. Parvin, F.; Faiz, J.; Qi, Y.; Kalhor, A.; Akin, B. A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks. IEEE Trans. Ind. Inform.; 2023; 19, pp. 10923-10933. [DOI: https://dx.doi.org/10.1109/TII.2023.3242773]

75. Luo, P.; Yin, Z.; Yuan, D.; Gao, F.; Liu, J. An Intelligent Method for Early Motor Bearing Fault Diagnosis Based on Wasserstein Distance Generative Adversarial Networks Meta Learning. IEEE Trans. Instrum. Meas.; 2023; 72, 3517611. [DOI: https://dx.doi.org/10.1109/TIM.2023.3278289]

76. Park, C.H.; Kim, H.; Suh, C.; Chae, M.; Yoon, H.; Youn, B.D. A Health Image for Deep Learning-Based Fault Diagnosis of a Permanent Magnet Synchronous Motor Under Variable Operating Conditions: Instantaneous Current Residual Map. Reliab. Eng. Syst. Saf.; 2022; 226, 108715. [DOI: https://dx.doi.org/10.1016/j.ress.2022.108715]

77. Wang, X.-B.; Luo, L.; Tang, L.; Yang, Z.-X. Automatic Representation and Detection of Fault Bearings in In-Wheel Motors Under Variable Load Conditions. Adv. Eng. Inform.; 2021; 49, 101321. [DOI: https://dx.doi.org/10.1016/j.aei.2021.101321]

78. Lang, W.; Hu, Y.; Gong, C.; Zhang, X.; Xu, H.; Deng, J. Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review. IEEE Trans. Transp. Electrific.; 2022; 8, pp. 384-406. [DOI: https://dx.doi.org/10.1109/TTE.2021.3110318]

79. Guo, Z.; Yang, M.; Huang, X. Bearing Fault Diagnosis Based on Speed Signal and CNN Model. Energy Rep.; 2022; 8, pp. 904-913. [DOI: https://dx.doi.org/10.1016/j.egyr.2022.08.041]

80. Dybkowski, M.; Kołodziej, A.; Klimkowski, K.; Orłowska-Kowalska, T. Application of Simplified Convolutional Neural Networks for Initial Stator Winding Fault Detection of the PMSM Drive Using Different Raw Signal Data. Energies; 2022; 15, 8051. [DOI: https://dx.doi.org/10.3390/en15218051]

81. Cai, B.; Hao, K.; Wang, Z.; Yang, C.; Kong, X.; Liu, Z.; Ji, R.; Liu, Y. Data-Driven Early Fault Diagnostic Methodology of Permanent Magnet Synchronous Motor. Expert Syst. Appl.; 2021; 177, 115000. [DOI: https://dx.doi.org/10.1016/j.eswa.2021.115000]

82. Liu, Z.; Fang, L.; Jiang, D.; Qu, R. A Machine-Learning-Based Fault Diagnosis Method with Adaptive Secondary Sampling for Multiphase Drive Systems. IEEE Trans. Power Electron.; 2022; 37, pp. 8767-8772. [DOI: https://dx.doi.org/10.1109/TPEL.2022.3153797]

83. Kao, I.-H.; Wang, W.-J.; Lai, Y.-H.; Perng, J.-W. Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning. IEEE Trans. Instrum. Meas.; 2025; 74, pp. 310-324. [DOI: https://dx.doi.org/10.1109/TIM.2018.2847800]

84. Karabacak, Y.E.; Gürsel Özmen, N. Common Spatial Pattern-Based Feature Extraction and Worm Gear Fault Detection Through Vibration and Acoustic Measurements. Measurement; 2022; 187, 110366. [DOI: https://dx.doi.org/10.1016/j.measurement.2021.110366]

85. Bacha, A.; El Idrissi, R.; Janati Idrissi, K.; Lmai, F. Comprehensive Dataset for Fault Detection and Diagnosis in Inverter-Driven Permanent Magnet Synchronous Motor Systems. Data Brief; 2025; 58, 111286. [DOI: https://dx.doi.org/10.1016/j.dib.2025.111286]

86. Jankowska, K.; Dybkowski, M. Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks. Designs; 2022; 6, 18. [DOI: https://dx.doi.org/10.3390/designs6010018]

87. Skowron, M.; Orlowska-Kowalska, T.; Kowalski, C.T. Detection of Permanent Magnet Damage of PMSM Drive Based on Direct Analysis of the Stator Phase Currents Using Convolutional Neural Network. IEEE Trans. Ind. Electron.; 2022; 69, pp. 13665-13675. [DOI: https://dx.doi.org/10.1109/TIE.2022.3146557]

88. Li, Y.; Wang, Y.; Zhang, Y.; Zhang, J. Diagnosis of Inter-Turn Short Circuit of Permanent Magnet Synchronous Motor Based on Deep Learning and Small Fault Samples. Neurocomputing; 2021; 442, pp. 348-358. [DOI: https://dx.doi.org/10.1016/j.neucom.2020.04.160]

89. Zhang, J.; Wang, Y.; Zhu, K.; Zhang, Y.; Li, Y. Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework. IEEE Trans. Ind. Inform.; 2021; 17, pp. 8495-8504. [DOI: https://dx.doi.org/10.1109/TII.2021.3067915]

90. Pietrzak, P.; Wolkiewicz, M. Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms. Sensors; 2023; 23, 1757. [DOI: https://dx.doi.org/10.3390/s23041757]

91. Morales-Perez, C.; Amezquita-Sanchez, J.P.; Valtierra-Rodriguez, M.; Rangel-Magdaleno, J.; Cerezo-Sanchez, J.; Leon-Bonilla, A. ITSC Fault Detection in Induction Motor using a Cosine Filter and a CNN Architecture. Proceedings of the 2024 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC); Ixtapa, Mexico, 11–13 November 2024; pp. 1-6. [DOI: https://dx.doi.org/10.1109/ROPEC62734.2024.10876974]

92. Wang, C.-S.; Kao, I.-H.; Perng, J.-W. Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning. Sensors; 2021; 21, 3608. [DOI: https://dx.doi.org/10.3390/s21113608]

93. Ewert, P.; Orlowska-Kowalska, T.; Jankowska, K. Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks. Energies; 2021; 14, 712. [DOI: https://dx.doi.org/10.3390/en14030712]

94. Pasqualotto, D.; Zigliotto, M. Increasing Feasibility of Neural Network-Based Early Fault Detection in Induction Motor Drives. IEEE J. Emerg. Sel. Top. Power Electron.; 2022; 10, pp. 2042-2051. [DOI: https://dx.doi.org/10.1109/JESTPE.2021.3115170]

95. Lv, W.; Xu, S.; Xie, H.; Chen, L.; Wu, S.; Zhu, T. Detection and Discrimination of Interturn Short and High-Resistance Connection Faults in PMSM Based on Multi-Channel CNN. Proceedings of the 2025 IEEE 8th International Electrical and Energy Conference (CIEEC); Changsha, China, 16–18 May 2025; pp. 1586-1591. [DOI: https://dx.doi.org/10.1109/CIEEC64805.2025.11116640]

96. Pietrzak, P.; Wolkiewicz, M.; Orlowska-Kowalska, T. PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network. IEEE Trans. Ind. Electron.; 2023; 70, pp. 5192-5202. [DOI: https://dx.doi.org/10.1109/TIE.2022.3189076]

97. Song, Q.; Wang, M.; Lai, W.; Zhao, S. On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM. IEEE Trans. Power Electron.; 2023; 38, pp. 2456-2467. [DOI: https://dx.doi.org/10.1109/TPEL.2022.3207181]

98. Mohammad-Alikhani, A.; Nahid-Mobarakeh, B.; Hsieh, M.-F. One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines. IEEE Trans. Ind. Electron.; 2024; 71, pp. 3083-3092. [DOI: https://dx.doi.org/10.1109/TIE.2023.3265054]

99. Pietrzak, P.; Wolkiewicz, M. On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm. Electronics; 2021; 10, 1786. [DOI: https://dx.doi.org/10.3390/electronics10151786]

100. Yan, H.; Peng, Y.; Shang, W.; Kong, D. Open-Circuit Fault Diagnosis in Voltage Source Inverter for Motor Drive by Using Deep Neural Network. Eng. Appl. Artif. Intell.; 2023; 120, 105866. [DOI: https://dx.doi.org/10.1016/j.engappai.2023.105866]

101. Khan, M.U.I.; Pathan, M.I.H.; Rahman, M.M.; Islam, M.M.; Chowdhury, M.A.R.; Anower, M.S.; Rana, M.; Alam, M.S.; Hasan, M.; Sobuj, M.S.I. . Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification. IEEE Access; 2024; 12, pp. 71566-71584. [DOI: https://dx.doi.org/10.1109/ACCESS.2024.3400913]

102. Guo, H.; Guo, S.; Xu, J.; Tian, X. Power Switch Open-Circuit Fault Diagnosis of Six-Phase Fault Tolerant Permanent Magnet Synchronous Motor System Under Normal and Fault-Tolerant Operation Conditions Using the Average Current Park’s Vector Approach. IEEE Trans. Power Electron.; 2021; 36, pp. 2641-2660. [DOI: https://dx.doi.org/10.1109/TPEL.2020.3017637]

103. Hang, J.; Shu, X.; Ding, S.; Huang, Y. Robust Open-Circuit Fault Diagnosis for PMSM Drives Using Wavelet Convolutional Neural Network with Small Samples of Normalized Current Vector Trajectory Graph. IEEE Trans. Ind. Electron.; 2023; 70, pp. 7653-7663. [DOI: https://dx.doi.org/10.1109/TIE.2022.3231304]

104. Tang, Y.; Zhang, X.; Zhai, Y.; Qin, G.; Song, D.; Huang, S.; Long, Z. Rotating Machine Systems Fault Diagnosis Using Semisupervised Conditional Random Field-Based Graph Attention Network. IEEE Trans. Instrum. Meas.; 2021; 70, 3521010. [DOI: https://dx.doi.org/10.1109/TIM.2021.3091212]

105. Saucedo-Dorantes, J.J.; Jaen-Cuellar, A.Y.; Delgado-Prieto, M.; Romero-Troncoso, R.J.; Osornio-Rios, R.A. Condition Monitoring Strategy Based on an Optimized Selection of High-Dimensional Set of Hybrid Features to Diagnose and Detect Multiple and Combined Faults in an Induction Motor. Measurement; 2021; 178, 109404. [DOI: https://dx.doi.org/10.1016/j.measurement.2021.109404]

106. Huang, W.; Du, J.; Hua, W.; Bi, K.; Fan, Q. A Hybrid Model-Based Diagnosis Approach for Open-Switch Faults in PMSM Drives. IEEE Trans. Power Electron.; 2022; 37, pp. 3728-3732. [DOI: https://dx.doi.org/10.1109/TPEL.2021.3123144]

107. Cai, B.; Wang, Z.; Zhu, H.; Liu, Y.; Hao, K.; Yang, Z.; Ren, Y.; Feng, Q.; Liu, Z. Artificial Intelligence Enhanced Two-Stage Hybrid Fault Prognosis Methodology of PMSM. IEEE Trans. Ind. Inform.; 2022; 18, pp. 7262-7273. [DOI: https://dx.doi.org/10.1109/TII.2021.3128245]

108. Li, Y.; Chen, Y.; Zhu, K.; Bai, C.; Zhang, J. An Effective Federated Learning Verification Strategy and Its Applications for Fault Diagnosis in Industrial IoT Systems. IEEE Internet Things J.; 2022; 9, pp. 16835-16849. [DOI: https://dx.doi.org/10.1109/JIOT.2022.3153343]

109. Wang, X.; Lu, S.; Chen, K.; Wang, Q.; Zhang, S. Bearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multisensor Data Fusion. IEEE Trans. Ind. Inform.; 2022; 18, pp. 2452-2463. [DOI: https://dx.doi.org/10.1109/TII.2021.3095086]

110. Chen, Y.; Zhao, Z.; Wu, H.; Chen, X.; Xiao, Q.; Yu, Y. Fault Anomaly Detection of Synchronous Machine Winding Based on Isolation Forest and Impulse Frequency Response Analysis. Measurement; 2022; 188, 110531. [DOI: https://dx.doi.org/10.1016/j.measurement.2021.110531]

111. Zhang, Y.; Mao, Y.; Wang, X.; Wang, Z.; Xiao, D.; Fang, G. Current Prediction-Based Fast Diagnosis of Electrical Faults in PMSM Drives. IEEE Trans. Transp. Electrif.; 2022; 8, pp. 4622-4632. [DOI: https://dx.doi.org/10.1109/TTE.2022.3195225]

112. Huang, F.; Zhang, X.; Qin, G.; Xie, J.; Peng, J.; Huang, S.; Long, Z.; Tang, Y. Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals. IEEE Trans. Ind. Inform.; 2023; 19, pp. 6105-6116. [DOI: https://dx.doi.org/10.1109/TII.2022.3165283]

113. Zhang, M.; Li, W.; Tang, H. Demagnetization Fault Diagnosis of the Permanent Magnet Motor for Electric Vehicles Based on Temperature Characteristic Quantity. IEEE Trans. Transp. Electrif.; 2023; 9, pp. 759-770. [DOI: https://dx.doi.org/10.1109/TTE.2022.3200927]

114. Wang, X.; Lu, S.; Huang, W.; Wang, Q.; Zhang, S.; Xia, M. Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis. IEEE Trans. Instrum. Meas.; 2021; 70, 3508612. [DOI: https://dx.doi.org/10.1109/TIM.2021.3051668]

115. Wang, H.; Zheng, J.; Xiang, J. Online Bearing Fault Diagnosis Using Numerical Simulation Models and Machine Learning Classifications. Reliab. Eng. Syst. Saf.; 2023; 234, 109142. [DOI: https://dx.doi.org/10.1016/j.ress.2023.109142]

116. Gmati, B.; Jlassi, I.; El Khil, S.K.; Marques Cardoso, A.J. Open-Switch Fault Diagnosis in Voltage Source Inverters of PMSM Drives Using Predictive Current Errors and Fuzzy Logic Approach. IET Power Electron.; 2021; 14, pp. 1059-1072. [DOI: https://dx.doi.org/10.1049/pel2.12098]

117. Zhou, Y.; Shang, Q.; Guan, C. Three-Phase Asynchronous Motor Fault Diagnosis Using Attention Mechanism and Hybrid CNN-MLP by Multi-Sensor Information. IEEE Access; 2023; 11, pp. 98402-98414. [DOI: https://dx.doi.org/10.1109/ACCESS.2023.3307770]

118. Huang, W.; Du, B.; Li, T.; Sun, Y.; Cheng, Y.; Cui, S. Interturn Short-Circuit Fault Diagnosis of Interior Permanent Magnet Synchronous Motor for Electric Vehicle Based on Search Coil. IEEE Trans. Power Electron.; 2023; 38, pp. 2506-2515. [DOI: https://dx.doi.org/10.1109/TPEL.2022.3213512]

119. Hang, J.; Ding, S.; Ren, X.; Hu, Q.; Huang, Y.; Hua, W.; Wang, Q. Integration of Interturn Fault Diagnosis and Torque Ripple Minimization Control for Direct-Torque-Controlled SPMSM Drive System. IEEE Trans. Power Electron.; 2025; 36, pp. 11124-11134. [DOI: https://dx.doi.org/10.1109/TPEL.2021.3073774]

120. Chen, H.; Gao, C.; Si, J.; Nie, Y.; Hu, Y. A Novel Method for Diagnosing Demagnetization Fault in PMSM Using Toroidal-Yoke-Type Search Coil. IEEE Trans. Instrum. Meas.; 2022; 71, 7501012. [DOI: https://dx.doi.org/10.1109/TIM.2021.3134991]

121. Shaikh, M.F.; Park, J.; Lee, S.B. A Non-Intrusive Leakage Flux Based Method for Detecting Rotor Faults in the Starting Transient of Salient Pole Synchronous Motors. IEEE Trans. Energy Convers.; 2021; 36, pp. 1262-1270. [DOI: https://dx.doi.org/10.1109/TEC.2020.3021207]

122. Rafaq, M.S.; Lee, H.; Park, Y.; Lee, S.B.; Fernandez, D.; Diaz-Reigosa, D.; Briz, F. A Simple Method for Identifying Mass Unbalance Using Vibration Measurement in Permanent Magnet Synchronous Motors. IEEE Trans. Ind. Electron.; 2022; 69, pp. 6441-6444. [DOI: https://dx.doi.org/10.1109/TIE.2021.3088332]

123. Gurusamy, V.; Bostanci, E.; Li, C.; Qi, Y.; Akin, B. A Stray Magnetic Flux-Based Robust Diagnosis Method for Detection and Location of Interturn Short Circuit Fault in PMSM. IEEE Trans. Instrum. Meas.; 2021; 70, 3500811. [DOI: https://dx.doi.org/10.1109/TIM.2020.3013128]

124. Bostanci, E.; Akin, B. Recent Trends in Magnetic Sensors and Flux-Based Condition Monitoring of Electromagnetic Devices. IEEE Trans. Ind. Electron.; 2021; 68, pp. 4052-4063. [DOI: https://dx.doi.org/10.1109/TIE.2020.2972452]

125. Im, J.-H.; Kang, J.-K.; Hur, J. Static and Dynamic Eccentricity Faults Diagnosis in PM Synchronous Motor Using Planar Search Coil. IEEE Trans. Ind. Electron.; 2023; 70, pp. 9291-9300. [DOI: https://dx.doi.org/10.1109/TIE.2022.3212402]

126. Glowacz, A. Thermographic Fault Diagnosis of Electrical Faults of Commutator and Induction Motors. Eng. Appl. Artif. Intell.; 2023; 121, 105962. [DOI: https://dx.doi.org/10.1016/j.engappai.2023.105962]

127. Luo, P.; Yin, Z.; Zhang, Z.; Zhang, Y.; Zhang, P.; Liu, J. Diversified Diagnosis Strategy for PMSM Inter-Turn Short-Circuit Fault via Novel Sliding Mode Observer. IEEE Trans. Power Electron.; 2024; 39, pp. 4149-4160. [DOI: https://dx.doi.org/10.1109/TPEL.2024.3352077]

128. Xu, S.; Chen, X.; Yang, W.; Liu, F.; Chai, Y. Current Sensor Incipient Fault Diagnosis in PMSM Drive Systems Using Novel Interval Sliding Mode Observer. IEEE Trans. Instrum. Meas.; 2024; 73, 3508211. [DOI: https://dx.doi.org/10.1109/TIM.2024.3351232]

129. Hang, J.; Qiu, G.; Hao, M.; Ding, S. Improved Fault Diagnosis Method for Permanent Magnet Synchronous Machine System Based on Lightweight Multisource Information Data Layer Fusion. IEEE Trans. Power Electron.; 2024; 39, pp. 13808-13817. [DOI: https://dx.doi.org/10.1109/TPEL.2024.3432163]

130. Wei, D.; Liu, K.; Wang, J.; Zhou, S.; Li, K. ResNet-18-Based Interturn Short-Circuit Fault Diagnosis of PMSMs with Consideration of Speed and Current Loop Bandwidths. IEEE Trans. Transp. Electrific.; 2024; 10, pp. 5805-5818. [DOI: https://dx.doi.org/10.1109/TTE.2023.3319157]

131. Lang, W.; Hu, Y.; Zhang, Z.; Gan, C.; Si, J.; Wen, H. Few-Shot Learning with Residual Current for EV Inverter Fault Diagnosis of EV Powertrain. IEEE Trans. Transp. Electrific.; 2024; 10, pp. 9316-9326. [DOI: https://dx.doi.org/10.1109/TTE.2024.3349619]

132. Bacha, A.; El Idrissi, R.; Lmai, F.; El Hassani, H.; Janati Idrissi, K.; Benhra, J. Advanced Deep Learning Approaches for Fault Detection and Diagnosis in Inverter-Driven PMSM Systems. Int. J. Adv. Comput. Sci. Appl.; 2024; 15, 912. [DOI: https://dx.doi.org/10.14569/IJACSA.2024.0151291]

133. Al-Haddad, L.A.; Jaber, A.A.; Hamzah, M.N.; Fayad, M.A. Vibration-Current Data Fusion and Gradient Boosting Classifier for Enhanced Stator Fault Diagnosis in Three-Phase Permanent Magnet Synchronous Motors. Electr. Eng.; 2024; 106, pp. 3253-3268. [DOI: https://dx.doi.org/10.1007/s00202-023-02148-z]

134. Huang, F.; Zhang, X.; Deng, X.; Qin, G.; Xie, J. Denoising Universal Domain Adaptation Method for PMSM Fault Diagnosis Under Category Inconsistency. IEEE Sens. J.; 2025; 25, pp. 15642-15652. [DOI: https://dx.doi.org/10.1109/JSEN.2025.3550558]

135. Meng, Q.; Shahvali, M.; Vrachimis, S.; Polycarpou, M.M. Fault-Tolerant Safe Control for Water Networks: A Backstepping Neural Control Barrier Function Approach. J. Process Control; 2025; 145, 103344. [DOI: https://dx.doi.org/10.1016/j.jprocont.2024.103344]