1. Introduction
The maintenance of the systems has recently become increasingly important for enhancing product efficiency and continuity. Different varieties of system maintenance exist, such as reactive, planned, proactive, and predictive [1]. Figure 1 summarizes them. Reactive maintenance only solves the issue when the system breaks down or malfunctions. The malfunction becomes apparent, and then the repairing steps are applied. Planned maintenance is previously scheduled to perform regular inspections and maintenance tasks at predetermined intervals to prolong the system’s life and reduce repair costs, regardless of whether the system has shown failure signs. Predictive maintenance (PdM) is an approach applying advanced analytics on the obtained data from multiple sensors to predict when the system tends to fail and organize the maintenance tasks accordingly to optimize maintenance intervals, reduce malfunction time, and enhance the system’s reliability.
PdM has shown significant growth and advancements. Most recently, low-cost sensors have been developed, and new real-time condition monitoring systems have been successfully used to obtain big data. These developments, expert algorithms, and expert human experience brought considerable developments and progress in predictive maintenance. Current efforts are given to developing new multivariate statistical models and expert algorithms to improve predictions’ accuracy and reduce labor costs [1,2,3,4,5,6,7,8,9,10,11]. Reaching next-step autonomy in a robotics system is possible thanks to sophisticated artificial intelligence (AI)-based algorithms, models, and expertise. Moreover, the potential AI-based PdM reduces costs and boosts efficiency and safety. Hence, the researchers pay special attention to AI models and techniques to improve the autonomy and adaptability of robotic systems in complex and dynamic industrial environments [12,13,14,15,16,17,18,19,20,21,22].
AI has been successfully employed in the automotive, manufacturing, energy, aerospace, and transportation industries by making real-time predictions and estimations of malfunctions and anomalies on big datasets [23]. It has been demonstrated that AI-based techniques, including machine learning and deep learning, exhibit improved performance and accuracy at PdM utilizing remaining useful life (RUL), fault diagnosis, and predictive maintenance scheduling [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43]. Using AI brings some challenges, such as transparency, explainability, system integration, and ethical issues [44], leading to explainable artificial intelligence (XAI) [45]. Figure 2 shows the general AI cover. This paper aims to comprehensively survey the current state-of-the-art techniques, challenges, and opportunities exhibited by AI-based PdM, focusing on key components, trustworthiness, and future trends. The most recent outcomes and innovations in the field are discussed in this paper by suggesting directions for further investigation. Finally, it gives some insights into the most recent research and advancements in the subject and assists in suggesting prospective areas for future research by providing a thorough overview of the existing literature.
Research Questions
This study examines current advances in AI-based PdM, particularly emphasizing next-generation autonomy. The following research inquiries are what this review is based on:
What are the main components of AI-based PdM systems?
What are the state-of-the-art (SOTA) PdM methods? Regarding accuracy, cost-effectiveness, and scale, what are their advantages?
What are the advantages of AI-based PdM techniques over traditional techniques regarding performance and cost-effectiveness?
What are the challenges and limitations of AI-based PdM?
How can AI-based PdM systems ensure high transparency and explanation?
How can AI be integrated into existing PdM systems and workflows?
What are the ethical issues in AI-based PdM?
How can an efficient human–machine interaction in AI-based PdM systems be obtained?
How can testing and validation of AI-based PdM systems be effectively conducted in real-world scenarios?
What are recent advances and future trends in AI-based PdM?
Taking in the research questions, the contributions of this review are (i) the description of the main components of AI-based PdM systems, (ii) the analysis of the SOTA methods in PdM, (iii) comparison of the AI-based PdM with traditional approaches, (iv) investigation of the challenges and limitations of AI-based PdM, (v) assessment of transparency and explainability in AI-based PdM, (vi) integration of AI into existing PdM systems and workflows, (vii) investigation of ethical issues related to AI-based PdM, (viii) enhancing human–machine interaction in the AI-based PdM systems, (ix) effective testing and validation of AI-based PdM systems, and (x) study of the AI-based PdM advances and emerging topics.
The rest of the paper is organized as follows: Section 2 comprehensively describes the key components of AI-based PdM. The SOTA for PdM and its enabling technologies are presented in Section 3. Then, Section 4 focuses on transparency and explainability in AI-based PdM. The challenges and limitations of using AI for PdM autonomy are highlighted in Section 5. Section 6 presents recent advances and future trends in AI-based PdM. Conclusions are given in Section 7.
2. Key Components in AI-Based Predictive Maintenance
AI-based PdM can be fundamentally broken down into six distinct components: data preprocessing, AI algorithms, decision-making modules, communication and integration, and user interface and reporting, as shown in Figure 3. This section briefly discusses each component to understand how they work together to enable AI-based PdM.
-
Sensors: Sensors are the frontline data collectors in a PdM system. These specialized devices are strategically placed on equipment and machinery to continuously monitor various parameters, such as temperature, pressure, vibration, and more. Sensor data provides real-time insights into equipment health and forms the foundation for predictive maintenance analysis.
-
Data Preprocessing: Raw data obtained from sensors often contains noise and inconsistencies. Data preprocessing is the initial step in preparing the data for analysis. It includes data cleaning, normalization, and missing data handling. High-quality data are essential for accurate PdM modeling.
-
AI Algorithms: AI algorithms, including machine learning and deep learning techniques, are the brain of the PdM systems. The algorithms analyze the data to identify the most important features relating to possible failures. They learn from historical data to predict equipment failures, anomalies, and RUL.
-
Decision-Making Modules: The insights and predictions generated by the AI algorithms are processed by decision-making modules. These modules are responsible for determining when maintenance actions are needed. They can recommend preventive or corrective maintenance tasks, schedule maintenance, and trigger alerts to maintenance teams when necessary.
-
Communication and Integration: Communication and integration ensure that the insights generated by the system are effectively translated into action. This component involves interactions with various stakeholders, including maintenance personnel and management. Furthermore, integration with enterprise systems such as ERP and asset management software aligns predictive maintenance with broader organizational goals.
-
User Interface and Reporting: To make these insights accessible to maintenance staff and decision makers, user interfaces and reporting tools are essential. The tools make it easier for users to understand complex data patterns and make informed decisions by providing data visualization, dashboard, and reporting capabilities. Data visualization tools and dashboards communicate data insights and forecast information to maintenance teams and decision makers. Visual aids help understand complex data patterns and make informed decisions.
The following three data-related units are added to the advanced AI methods to obtain resilient, reliable, secure, and highly stable results using AI-based PdM in complex and dynamic environments.
Sensor data and the Internet of Things (IoT) integration: Integrating IoT and sensor technology is pivotal in next-step autonomy for PdM tasks. The IoT sensors are strategically placed in equipment and machines to monitor their condition in real-time continuously.
Data integration: Data integration combines data from various sources, including historical maintenance records, real-time sensor data, external factors (e.g., weather), and production schedules. This holistic view of equipment health enhances decision making.
Digital twins: Digital twins create virtual replicas of physical assets, facilitating real-time simulation and monitoring. AI systems monitor these digital twins, identifying performance irregularities and recommending optimal maintenance strategies before any physical equipment is adversely affected.
Edge and cloud computing: Edge computing proceeds closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. Cloud computing stores and manages enormous amounts of data to analyze historical events.
3. State-of-the-Art Techniques for Predictive Maintenance
SOTA consists of (i) the AI-based PdM approaches, including machine learning algorithms, deep learning, statistical control, and statistical modeling, (ii) data-driven approaches, including big data analytics, data mining, data visualization, and predictive data analysis, (iii) vibration and thermal analysis, (iv) augmented reality (AR), virtual reality (VR), mixed reality (MR), and their extended versions, and digital intelligent assistants, (v) methods based on prescriptive maintenance, (vi) edge and cloud computing, IoT, federated learning, and blockchain, (vii) energy-based methods, and (viii) methods based on prognostics and health management (PHM) [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131]. This section reviews some PdM studies using these approaches. Each paper has been evaluated through the application and the proposed approach.
PdM applications employ all AI approaches, including the classification and/or regression problem type of the supervised learning approach, the clustering problem type of the unsupervised learning approach, or a problem type relating to the reinforcement learning (RL) approach to analyze the large volume of data obtained from real-time condition monitoring systems. The approaches have presented magnificent contributions to PdM tasks [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52]. Deep neural networks (DNNs) covering CNNs, RNNs, and LSTMs have been used to generate the proposed predictive maintenance strategies and algorithms and improve their prediction accuracy [53,54,55,56,57,58,59,60,61,62,63,64]. In these works, the networks have been applied to extract the significant features from raw sensor data, including images or the data in the form of time series, and to detect, recognize, or predict sudden and expected changes in the system according to the features.
Machine learning algorithms have been successfully applied to many PdM applications [65,66,67,68,69]. Algorithms such as feedforward neural networks (FNNs), decision trees (DT), random forests (RF), and support vector machines (SVMs) have been used to classify sensor data.
Refs. [70,71,72,73,74,75,76,77,78,79,80,81,82,83,84] have shown various works applying statistical and probabilistic modeling approaches such as hidden Markov models (HMMs), Bayesian networks (BNs), Gaussian mixture models (GMMs), extreme gradient boosting (XGBoost), Density-based spatial clustering (DBSC), principal component analysis (PCA), and K-means to PdM tasks. Moreover, they introduced different DNN models, such as LSTM and autoencoders, for the tasks. They have identified the deterioration event by analyzing an interdependent relationship among the data collected from multi-sensors and predicted possible future failures.
In [85,86,87,88,89,90,91,92,129], data-driven approaches, including big data analytics relating to data from various sources, including sensor data, historical records, external factors, and data mining, have been used to improve the accuracy and comprehensiveness of PdM systems. Significantly, interactive and intuitive data visualization tools have contributed to quickly understanding the equipment’s health and making informed decisions.
In [93,94,95,96,97,98,99], vibration analysis is a widely used method of PdM. The analytical method uses sensors to measure the vibrations of machinery and identify possible problem areas, such as bearing failure or misalignment on various machines, including motors, pumps, and gearboxes.
Thermal imaging is another widely known technique for PdM [100,101,102,103,104]. The technique uses infrared cameras to perceive potential problems, such as overheating, breakdown, friction, and energy inefficiencies, on a wide range of equipment, including electrical panels, transformers, and motors, by measuring the temperature of equipment.
In [105,106,107,108], the technologies of AR, VR, MR, digital intelligent assistant, digital twin, and IoT sensors have been leveraged. The IoT sensor has enabled real-time data collection from various sensors attached to equipment in PdM. The technologies of AR, VR, and MR increase the capabilities of maintenance technicians with visual guidance, remote assistance, real-time information, and a virtual view of equipment status to perform PdM, improving efficiency and accuracy. Ref. [109] showed an approach for maintenance experts and operators to interact with a PdM system by AI intelligent assistant through natural language processing (NLP) and user feedback about the success of maintenance interventions.
In [110,111,112,113,114,115], PdM is realized through prescriptive maintenance. It provides specific recommendations for maintenance actions going beyond prediction. It includes detailed instructions for technicians on what steps to take. This approach predicts when maintenance is needed and recommends the most effective and efficient maintenance actions.
Refs. [116,117,118,130,131] have used edge computing and cloud computing. Edge computing is used to proceed closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. To analyze historical events, cloud computing stores and manages enormous amounts of data.
In [119,120,121], federated learning, blockchain, and industrial IoT have been used. Federated learning allows multiple parties to train a machine learning model collaboratively without sharing their data. Blockchain ensures data integrity, while industrial IoT sensors provide real-time equipment condition data. It may be indicative of problems if unusual energy consumption is observed. In [122,123,124,125], PdM has been provided by analyzing their energy consumption patterns to gain information on the health of such devices.
In [126,127], PdM has been carried out by focusing on PHM. PHM techniques take a different approach to assessing equipment health from predicting failures. Advanced modeling and data analysis are generally included in these methods, enabling further RUL estimation of an asset and providing valuable insight into maintenance planning. Ref. [128] shows an example of a predictive analytics software platform for PdM. In order to make implementing PdM easier, numerous software platforms and tools have been developed. Premade models and features for data integration are frequently included in these platforms.
This article reviews real-world simulation and experimental applications in tables, while some utilize benchmark datasets. Table 1 describes the benchmark datasets such as the National Aeronautics and Space Administration (NASA) Turbofan [132,133], PHM 2008 [134], NASA Ames Milling [135], NASA Bearing Dataset [136], CWRU Bearing [137], FEMTO Ball Bearing [138], Roll Bearing [139], Backblaze [140], PAKDD2020 Alibaba AI OPS [141], NASA Ames Prognostics [142], Lithium-ion Battery of University of Maryland [143], MOSFET Thermal Overstress Aging [144], MAFAULDA [145], Microsoft Azure PdM [146], GEFCOM [147], and The UCI SECOM [148] in multivariate time-series forms commonly employed in PdM applications.
Table 2 shows a taxonomy for PdM applications in different problem types/approaches from various industries. The second column of Table 2 mainly indicates a class name relating to the repair and maintenance activities from [149]. Table 3 summarizes the study numbers in Table 2. As seen in Table 3, the electrical equipment class of the repair and maintenance field is the most popular in AI and maintenance, with 30 studies focused on this field. The machinery category is an essential topic with ten studies and attracts wide attention in this field. The classes of electronic and optical equipment and fabricated metal products attract attention with nine and eight studies, respectively.
The transport equipment category in Table 3 is noted as an essential field with six studies. The other machinery and equipment category is also notable with five works. On the other hand, the categories computers and communication equipment, industrial machinery and equipment, and buildings and other structures have a lower level of interest, with one study each, respectively. As a result, industries such as electrical equipment, machinery, electronic and optical equipment, and fabricated metal products are popular areas for AI and maintenance. On the other hand, computers and communication equipment, industrial machinery and equipment, and buildings and other structures categories have received more limited attention. These results are essential guidance for directing future research and development efforts.
On the other hand, manufacturing industries, including automotive, aircraft, semiconductor, machine tools, gearbox, pharmaceutical, electronics, steel, robotics, industrial robotics, air conditioning, and industrial robots, are the industry sectors receiving the most attention in AI and maintenance, with 22 studies. This wide-ranging industry shows a significant research focus as it includes a variety of subsectors.
The industries of ‘electrical and electronics’ and ‘electronics’ also have attracted attention with 13 studies. Technological advances and maintenance practices have been reflecting significant interest in this industry. The transport, automotive, high-speed railway, autonomous vehicles, railways, and rail transport industries represent an important research area with ten studies. The combination of subsectors such as automotive, high-speed rail, and autonomous vehicles shows a wide range of topics covering various aspects of this industry. The industries of ‘metals and plastics’ and ‘metal and metallurgy’ have five studies. The energy and sustainability, nuclear power, nuclear energy, renewable energy, and wind energy industry is also a prominent field with five studies and covers a wide range of topics related to energy sustainability.
Information technology, cloud services and data centers, electric vehicle battery technology, medical, medical devices and healthcare services, large service management, building and construction, hydraulics, and architecture, engineering, construction, and facility management industries are important industries represented with two studies. A limited number of studies represent infrastructure, pumping systems, marine, textile, logistics and parcel delivery, steel strip processing, chemical industry, aviation, and home energy management.
In addition, Table 3 also shows different PdM tasks and study numbers. RUL, cost, and charge estimation tasks have attracted attention with 11 studies. These tasks cover strategically essential issues such as estimating the useful life of equipment and determining maintenance costs. Fault detection tasks are a vital topic, with 12 studies focusing on the early detection and prevention of malfunctions that may occur in systems. The condition and vibration monitoring task has attracted the attention of nine studies. The anomaly detection task is a distinct area with four studies focusing on detecting unexpected situations and abnormal behavior. Other tasks are represented by only one study each and seem to concentrate on more specific topics. These tasks include product lifecycle management, component fatigue strength prediction, PdM by a digital intelligent assistant, blockchain framework for PdM, and a big data analytics framework. As a result, basic tasks such as failure prediction, RUL estimation, fault detection, and anomaly detection are popular areas of AI and maintenance. In contrast, other tasks are more specialized and focus on specific topics.
Although PdM tasks often appear to be primarily classification and regression problems in Table 3, the presence of unknown events and the general nature of the data with ambiguous labels clearly shows the importance of unsupervised learning, reinforcement learning, and statistical and probabilistic applications in PdM applications.
In addition, Table 2 includes some PdM tasks without using AI. Ref. [89] includes production quality prediction in the textile industry. Refs. [101,102,103] use thermal imaging technology in railways, medical, and hydraulics industries. Ref. [110] applies data management, predictive data analytic toolbox, recommender and decision support dashboard, and semantic-based learning and reasoning for PdM in the automotive manufacturing industry. Ref. [114] introduces the optimal maintenance strategy applying reliability analysis, sensitivity analysis, and a continuous stochastic process in the rail transport industry. Ref. [115] applies a discrete-event simulation framework for post-prognostic decisions in the aviation industry. Ref. [128] introduces a predictive analytics software platform in the manufacturing industry. Moreover, Ref. [91] includes quality management in the vinyl flooring industry using big data analytics and optimization and edge computing without having a PdM task, but it is related indirectly.
Figure 4 and Figure 5 show the number of studies carried out using different machine learning methods such as FNN, CNN, LSTM, autoencoder, SVM, RNN, fuzzy, RF, K-means, decision trees, rule-based intelligence system, DR, HMM, Bayes, ELM, and gradient boosting in Web of Science and Google Scholar for 2018 to 2023, respectively. Total study numbers in Web of Science and Google Scholar have been determined as (67, 104, 149, 213, 296, and 255) and (4406, 6506, 10,498, 16,638, 22,619, and 28,069) over 2018–2023, respectively. Both the results in Google Scholar and Web of Science clearly show that the fusion of PdM and AI will continue over the years. Since 2019, there has been a notable increase in the use of deep learning methods, specifically LSTM, CNN, and autoencoder, with a particularly pronounced surge in the utilization of DRL in the last two years. Additionally, SVM has maintained its relevance since 2018, and its usage has continued to grow. Another remarkable result has been observed in decision trees. The use of decision trees has shown a consistent upward trend since 2018. Notably, there has been a visible increase in the adoption of fuzzy and rule-based methods. Moreover, HMM, Bayes, gradient boosting, and FNN also exhibit a growing trend in usage since 2018. Even these escalating values in fundamental AI methods underscore the inevitability of AI becoming indispensable in future research endeavors. All the mentioned subjects show current relevance and an exponential increase has emerged, which signifies a robust and growing interest in these topics, indicating that AI will be indispensable in future studies. As a result, the figures exhibit an evolving landscape characterized by an increasing breadth of methods employed in PdM applications, suggesting a continuous effort by researchers to explore and integrate diverse machine learning methods, indicating the adaptability of the field and the ongoing quest for innovative approaches.
Figure 6 and Figure 7 show the number of studies carried out relating to four recent advances in PdM, including (PdM, digital twin, and AI), (PdM, IoT, and AI), (PdM, edge and cloud computing, and AI), and (PdM, AR, VR, MR, and AI) in Google Scholar and Web of Science, respectively. The total number of studies relating to the four technological combinations in Web of Science and Google Scholar has been determined as (2, 15, 24, 42, 50, and 40) and (1184, 2102, 3542, 5554, 7684, and 9867) over 2018–2023, respectively. The number of studies has a consistent annual increase. The observed growth proves a rising interest in incorporating advanced technologies into PdM applications and the shifts in the focus of researchers, reflecting evolving priorities in the field. In addition, the numbers indicate that (PdM, digital twin, and AI) and (PdM, IoT, and AI) have been gaining momentum since 2018 and that the use of (PdM, Edge and cloud computing, and AI) and (PdM, AR, VR, MR, and AI) has increased in the past three years and will increase in the future. As a result, the upward direction of the study numbers points to a positive inclination to integrate innovative techniques in PdM. Researchers are encouraged to explore the potential of digital twin, IoT, edge and cloud computing, AR, VR, MR, and AI for PdM applications.
Table 4 compares various AI methods. As observed, deep learning methods exhibit high accuracy and scalability. On the other hand, machine learning methods show higher cost-effectiveness in comparison. When comparing traditional methods with AI approaches, as in Table 5, AI methods outperform in stability and scalability. However, in terms of computational cost, traditional methods are more efficient.
4. Transparency and Explainability in AI-Based Predictive Maintenance
Model interpretability is critical to AI, as it allows us to gain insights into complex models’ black-box nature and build trustworthy models. Various techniques have emerged to show how these models make predictions and decisions. Ref. [150] exhibited two concepts titled explainability and interpretability used in the XAI area. If the model’s design is understandable to a human being, it is considered interpretable. On the contrary, explainability ties into the idea that explanation is a form of interaction between humans and decision makers. Explainability is regarded as a post hoc tool because this specification covers the techniques used to transform an uninterpretable model into an explainable one.
On the other hand, ref. [151] mentioned that if the models are inherently interpretable, they have their own explanations, which align with the model’s calculation. This implies that interpretability inherently includes explainability. In the interpretable model, each step of the decision-making process can be traced. Still, there remains a gap in explaining why this specific sequence of steps was chosen during the decision-making process. Explainable models are interpretable by default, but the reverse is not always true [152], indicating that explainability is a subset of interpretability. In explainable AI systems, it can be challenging to understand how the model reached a decision, but it can grasp the underlying reasoning behind it. XAI takes a broader approach, striving to design inherently transparent and interpretable models for humans.
The interpretability techniques are classified in different ways. One way is to separate them into two branches, post hoc and ad hoc, concerning the training process [153]. Post hoc techniques use external tools to analyze the trained models. The techniques rely on input perturbations. That is why they can provide unreliable results in cases like adversarial attacks, as in [154].
Moreover, they are not concerned about the model’s inner dynamics and the actions to generate different features. Ad hoc techniques modify the model’s inner dynamics to facilitate understanding, and the model comes with some explanations during the training itself. The post hoc techniques require feature analysis, model inspection, saliency maps, proxy models, mathematical analysis, physical analysis, and explanation by using text and case to extract the explanations. They are divided into “model agnostic” and “model specific”. Model-agnostic models also use any model covering neural networks.
On the other hand, model-specific techniques are developed as specific to the model. Some examples of post hoc techniques are local interpretable model-agnostic explanations (LIME) [155] and SHapley Additive exPlanations (SHAP) [156], class activation map (CAM) [157], layer-wise relevance propagation (LRP) [158], and gradient-weighted class activation mapping (Grad-CAM) [159]. Ad hoc methods include approaches to explaining the model in an explicitly understandable manner, using hand-crafted criteria for the selection of features or incorporating heuristics based on current physics [160,161].
Ref. [162] also classified the interpretation techniques as local and global. Local interpretability provides single predictions, whereas global predicting provides interpretation to make new predictions from model features. Local methods such as LIME and SHAP provide model-agnostic means to explain and interpret the predictions generated by a wide range of machine-learning models. Deep learning models, known for their complexity, benefit from techniques like Grad-CAM and attention mechanisms, which offer insights into the input regions that influence their decisions.
Some approaches to achieving model interpretability include techniques such as partial dependence plots (PDP) [163], decision tree visualization [163,164], attention module [165], and feature importance calculation by using methods such as wrapper, filter, embedded, and dimension reduction techniques [166]. PDPs represent a method of showing the relationship among one or several components and the expected outcomes of machine learning models. They give us an insight into how changes affect model predictions for a given feature. The structure of the decision tree, the observed features split, and the way the tree makes decisions based on the input features are displayed in the decision tree visualization.
On the other hand, the attention module contains a mechanism for generating feature weights consisting of parallel hidden layers. Each module part provides a value between 0 and 1, which shall be multiplied by the suitable feature to enter the rest of the network. The module can be visualized. Feature importance helps understand the features with the most weight in the model’s predictions. Wrapper methods are trained with various input features to obtain the best results using heuristic and sequential search algorithms, which is time consuming [167]. Filter methods employ statistical metrics such as the Pearson correlation coefficient, mutual information, and X2 test prior to training to identify the importance of features. The techniques are unrelated to the model or its predictions after training, and they have no interest in interactions between features [168]. Embedded methods work on the subsets of the data along with techniques such as random forest, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, regularization, decision tree, and XGBoost [169,170]. Dimension reduction techniques such as PCA, ICA (individual component analysis), linear discriminant analysis (LDA) reduce the dimensionality of the dataset to obtain a smaller set of principal components that explain most of the variance in the data [171].
In the literature, there are a lot of other methods, such as rule-based systems and sensitivity analysis. Rule-based systems utilize logical rules to describe the decision-making process of a model, making the decision logic explicit and understandable [172,173]. Lastly, sensitivity analysis helps identify the most influential input variables, contributing to a clearer understanding of a model’s predictive behavior. In an era where AI plays an increasingly prominent role, these interpretability techniques and methods are indispensable tools for building trust, improving model performance, and ensuring ethical and accountable AI systems.
Table 6 summarizes interpretable and explainable studies between 2018 and 2023 and their application content. In [174,175,176,177,178,179,180,181,182,183], SHAP, LIME, ELI5, integrated gradients, SmoothGrad, and LRP have been used. Refs. [184,185,186,187,188,189] use the semantic web rule language, rule-based expert systems, fuzzy systems, quantitative association rule mining (QARM), and data-driven sensitivity analysis. In [190,191,192,193,194,195,196,197,198,199,200,201,202,203,204], some techniques and visualizations such as decision trees, graph-based approaches, the attention modules used together with LSTM, generative adversarial networks (GAN), PDPs, and feature importance calculation methods. Refs. [205,206,207,208] give some new algorithms, including interpretability and explainability, such as temporal fusion separable convolutional network, federated learning, HMM, and reinforcement learning.
An extensive application range of PdM consists of a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter, the MW load range in a petrochemical plant, a real hybrid bus, hydraulic systems, a modular aero-propulsion system, an intelligent manufacturing system, the water pumping industry, a large gas distribution network, rolling bearings and a rotating machine, PdM for autonomous underwater vehicles (AUV), a water injection pump, wind turbine systems, the IoT-based manufacturing environment, hard disk drives, the turbofan engine, the drilling machine of an automotive manufacturer, solar photovoltaic energy systems, maintenance work orders, manufacturing, and structural health monitoring.
Figure 8 and Figure 9 show numbers relating to PdM and explainable/interpretable AI in Web of Science and Google Scholar for 2018 to 2023, respectively. The total study numbers in Web of Science and Google Scholar are (6, 5, 9, 30, 34, and 18) and (2328, 3058, 4166, 5834, 7350, and 6540) over 2018–2023, respectively. All numbers show significant growth in using both PdM and explainable/interpretable AI over the specified time frame. The increasing number of studies presents a rising interest and emphasis on these subjects and methods. In addition, the numbers emphasize the importance of PdM and the need for AI systems that are transparent and interpretable. The numbers support that using PdM and explainable/interpretable AI will continue to expand and generate a broader shift in the research focus on these areas.
5. Challenges and Limitations of Using AI for PdM Autonomy
Several problems and limitations that need to be overcome to realize AI’s potential benefits are posed by its use for PdM autonomy. Some key challenges and limitations are (i) transparency and explainability, (ii) integration with existing systems and workflows, (iii) data quality and quantity, (iv) the lack of real-world data, (v) the lack of standard evaluation metrics, (vi) ethical considerations, and (vii) effective human–machine interaction.
Transparency and explainability could make understanding and believing the decisions adopted in this system difficult for human operators, leading to mistrust and lack of acceptance among users. All developments about transparency and explainability are given in Section 4. More development should be undertaken to gain the trust of human operators in PdM applications.
The seamless integration into existing systems and workflows requires specialized software and expertise. Moreover, the different types of data formats, communication methods, and protocols create difficulties in integrating AI systems into current systems. There are some approaches for the seamless integration of AI-based PdM systems into current systems and workflows [209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224]. In [209,210,211,212,213,214,215,216,217,218,219,220,221], a modular design has been applied. The design allows the AI-based system to be flexibly integrated with existing systems and workflows and adaptable using standard interfaces and protocols, such as Modbus, Snap7, and OPC-UA, to communicate with existing systems and develop APIs. Refs. [223,224] has leveraged a service-oriented architecture (SOA) for integration. Web services, such as RESTful APIs, to access the AI-based system and message-oriented middleware, such as MQTT, to communicate with existing systems have been applied. Additionally, some researchers have proposed using edge computing and fog computing to integrate AI-based PdM systems into existing systems [219,220,221,222].
Data quality and quantity are crucial for training and validating AI-based PdM models. High-quality data provides accurate predictions. In contrast, the time and costs of gathering and cleaning data may be considerable. In addition, it is essential to have large amounts of data for learning deep learning models, and in certain cases, they may be challenging to obtain [85,86,129,192,210]. AI-based PdM systems should be tested and validated under various conditions, such as different types of equipment, different operating conditions, and different levels of data quality for successful real-world applications. The lack of real-world data makes it difficult to test the system under realistic conditions and to evaluate its performance and accuracy. Researchers have proposed various methods for simulating real-world scenarios to overcome this challenge, such as using virtual environments and testbeds [26,35,37,42,96,109,210,225,226,227,228,229,230,231].
The lack of standard evaluation metrics is another challenge in testing and validating AI-based PdM systems. The shortcoming makes comparing different systems’ performance and evaluating their accuracy and reliability difficult. Researchers have proposed various evaluation metrics to overcome this challenge, such as prediction accuracy, mean squared error, and precision and recall [24,232].
Ethical considerations are essential for AI-based PdM systems to ensure they are robust, reliable, and secure. Additionally, ethical considerations are associated with using AI-based PdM systems, such as transparency and explainability, trust and acceptance among users, integrating AI-based systems with existing systems and workflows, testing, validating, and data privacy and security [221,233,234].
Human–machine interaction is an essential aspect of AI-based PdM as it involves the interaction between human operators and autonomous systems. The goal of human–machine interaction in PdM is to enable human operators to monitor, control, and interact with autonomous systems safely, efficiently, and effectively. One of the critical challenges in HRI for AI-based PdM is the development of intuitive and user-friendly interfaces [235,236]. Researchers have proposed various methods for designing intuitive and user-friendly interfaces to overcome these challenges, such as virtual and augmented reality gestures and NLP [11,19,21,26,48,50,105,106,108,230]. Another challenge in HRI for AI-based PdM is the development of trust and acceptance among human operators. Generally, ML models, especially deep learning models, provide decisions that are too complicated for humans to understand, reducing people’s trust in their predictions. That is why developing simplified and interpretable models is vital [182]. Recent works have proposed to use NLP and, its more powerful version, generative AI, including large language models (LLMs), such as ChatGPT, PaLM, and Llama [237,238,239,240], to predict component failure or service requirements and to analyze historical log data that include equipment performance, environmental conditions, and maintenance schedules [241,242,243,244,245]. Ref. [246] has used NLP, dimension reduction, and clustering techniques for the PdM of an aircraft by using previous reports. A human collaboration with ChatGPT has applied PdM for mobile firefighter turnout gear cleaning in [247]. Ref. [248] has applied GPT and RL to control an HVAC system. Ref. [249] has proposed to leverage ChatGPT in different areas of supply chain management, such as route optimization, predictive maintenance, and order shipment. Ref. [250] has proposed a new language model for network traffic, including PdM in telecommunication. Critical AI training, testing, and diligence methods for PdM in automotive projects have been introduced in [251]. In [252], LLMs have been investigated for the failure mode classification task, an essential maintenance step. The works in [241,242,243,244,245,246,247,248,249,250,251,252] have shown that NLP and LLMs have increased predictive maintenance efficiency and accuracy since they can be constantly updated with real-time equipment data, enabling them to learn the patterns associated with healthy operational functioning.
6. Recent Advances and Future Trends in AI-Based PdM
Recent advances in AI-based PdM have improved the performance and accuracy of predictive maintenance predictions and increased the autonomy and adaptability of machines in complex and dynamic working environments. Some recent advances in this field include the following:
Integration of advanced machine learning algorithms;
Edge and cloud computing for real-time analysis and data storing;
Predictive analytics with big data;
XAI for transparency;
IoT sensor integration;
Digital twin, AR, VR, MR, and extended versions.
The field of AI-based PdM has been developing and improving, as shown in Figure 10. The future research topics in the field are given below:
Big data and analytics are used to collect, analyze, and interpret large amounts of data.
The exponential growth of cyber–physical systems of digital twins, AR, VR, XR, metaverse, and human-driven industrial metaverse solutions to both physical and virtual work environments allows smooth collaboration and communication between employees, machines/robots, and AI.
Development of autonomous maintenance systems that are capable of self-diagnosis, decision making, and proactive interventions without human intervention.
Evolving toward zero-touch maintenance operations where AI systems automate the maintenance process from detection to resolution.
Extraction of actionable insight advancements in AI algorithms to predict failures and provide actionable insights and recommendations for optimal maintenance strategies.
Integrating experiential learning and reinforcement learning techniques to improve AI models based on ongoing data and continuous feedback.
Implementation of blockchain technology for data security to enhance the security and integrity of PdM data, ensuring trust and transparency.
Development of trustworthy AI algorithms and human-centric AI interfaces for better collaboration between AI systems and human operators, facilitating seamless interaction and decision making.
Development of energy-efficient AI-based PdM to minimize resource consumption while maintaining high prediction accuracy.
Development of collaborative robots (cobots), IIoT, edge and cloud computing, and 5G/6G connectivity for next-step PdM autonomy and smart factory that can adjust to shifting circumstances and changing conditions and streamline manufacturing processes.
Development of generative AI models to contribute to the above items. For example, they can provide failure warnings, present encompassing instructions for repair and replacement methodologies, achieve suggestions to optimize energy consumption and cut down the carbon footprint to human operators by simulating the real system and/or analyzing maintenance logs and sensor data, and facilitate better collaboration between automated systems and human operators through natural language communication in automated maintenance planning.
7. Conclusions
This paper has reviewed the recent developments in AI-based PdM, focusing on next-step autonomy in robots. SOTA, challenges, and opportunities associated with AI-based PdM have been analyzed. The ethical considerations, integration, testing, and validation of AI-based PdM in real-world scenarios and human–machine interaction have also been discussed. The potential benefits of AI-based PdM, such as cost savings, increased efficiency, and improved safety, have been highlighted. It has been concluded that PdM is trustworthy thanks to explainable and interpretable AI for human operators. Therefore, AI is the main component of PdM for next-step autonomy in machines, which can improve the autonomy and adaptability of machines in complex and dynamic working environments. Finally, recent advances and future trends, including the use of generative AI models, have been addressed for further improvements and developments of the AI-based PdM.
Conceptualization, A.U. and M.K. validation, A.U., M.K. and N.K.; formal analysis, A.U., M.K. and N.K.; writing—original draft preparation, A.U. and M.K.; writing—review and editing, A.U., M.K. and N.K.; visualization, A.U. and M.K.; project administration, N.K. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
Necim Kırımça was employed by the Albayrak Makine Elektronik A.S, in Eskisehir, Turkey. 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.
Enlarge this image.Figure 4. The number of different AI methods in Web of Science between 2018 and 2023.
Enlarge this image.Figure 5. The number of different AI methods in Google Scholar between 2018 and 2023.
Enlarge this image.Figure 6. The number of studies relating to PdM, AI, IoT, edge and cloud computing, AR, VR, and MR in Web of Science between 2018 and 2023.
Enlarge this image.Figure 7. The number of studies relating to PdM, AI, IoT, edge and cloud computing, AR, VR, and MR in Google Scholar between 2018 and 2023.
Enlarge this image.Figure 8. The number of studies relating to PdM and explainable/interpretable AI in Web of Science between 2018 and 2023.
Enlarge this image.Figure 9. The number of studies relating to PdM and explainable/interpretable AI in Google Scholar between 2018 and 2023.
Benchmark datasets for PdM tasks.
| Ref. | Name | Description |
|---|---|---|
| [ | NASA Turbofan Dataset-CMAPSSD and CMAPSSD-2 | The turbofan engine degradation simulation dataset, generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. |
| [ | PHM 2008 Dataset | The degradation collected from aircraft engines derived from CMAPSSD. |
| [ | NASA Ames Milling Dataset | Acoustic emission, vibration, and motor current data collected under different experimental conditions for predicting the milling tool wear. |
| [ | NASA Bearing Dataset | Run-to-failure vibration data from 4 accelerometers in a shaft. |
| [ | Case Western Reserve University (CWRU) Bearing Dataset | Test rig operating with different load conditions. |
| [ | FEMTO Ball Bearing Dataset from IEEE PHM Challenge | Run-to-failure temperature and vibration data from engine thermocouple and accelerometer sensors. |
| [ | Roll Bearing Dataset from IEEE PHM Challenge | A training set of six rolling bearings operated in three different conditions, and a testing set of 11 more. |
| [ | Backblaze Hard Disk Drive Dataset | The daily status of hard disk drives (HDDs), consisting of 433 failed drives and 22,962 good drives. |
| [ | PAKDD2020 Alibaba AI OPS Competition Dataset | HDD daily health status data including both a raw and a normalized value as well as a label and the time of failure. |
| [ | NASA Ames Prognostics Dataset | Li-ion battery degradation data during repeated charge and discharge cycles. |
| [ | Lithium-ion Battery Dataset of the University of Maryland | The current and voltage data on different EV drive cycles at varying ambient temperatures (including 0 °C, 25 °C, and 45 °C). |
| [ | MOSFET Thermal Overstress Aging Dataset | Run-to-failure experiments on power |
| [ | MAFAULDA | Fault measurements from machinery simulators run under different load conditions. |
| [ | Microsoft Azure PdM Dataset | Data modules of machines, telemetry, errors, maintenance, and failures collected by a Microsoft employee for PdM modeling collection. |
| [ | Global Energy Forecasting Competition (GEFCOM) Dataset | Hourly solar power generation data and assigning numerical weather forecasts from 1 April 2012 to 1 July 2014. |
| [ | The UCI SECOM Dataset | Measurements of features of semiconductor production within a semiconductor manufacturing process. |
Summary of different AI techniques in PdM systems.
| Ref. | Class of Repair and Maintenance | Industry | Application | Problem Type/ | Algorithm/Technology |
|---|---|---|---|---|---|
| [ | Electrical equipment | Electrical and electronics | Aging monitoring for twisted pair specimens in low-voltage stator windings of electrical machines | Regression | 2D-CNN |
| [ | Computers and communication equipment | Information technology | RUL estimation on Microsoft Azure AI-based PdM dataset [ | Regression | CNN, RNN, LSTM, CNN-LSTM, regression random forest (RRF), deep feed-forward (DFF) networks, and gated recurrent unit (GRU) |
| [ | Electrical equipment Electronic and optical equipment | Electrical and electronics | Failure prediction on PAKDD2020 Alibaba AI OPS competition [ | Classification | CNN and time-series encoding techniques |
| [ | Electrical equipment | Metals and plastics | Health monitoring on NASA milling dataset [ | Regression | 1D CNN |
| [ | Electrical equipment | Metals and plastics | RUL estimation on FEMTO bearing dataset [ | Regression | LSTM and autoencoder |
| [ | Electrical equipment | Diagnosis and classification of faults in rotating machinery using MAFAULDA dataset [ | Classification | CNN | |
| [ | Electrical equipment | Metals and plastics | Fault diagnosis on the experimental data collected from a rotor fault diagnosis experimental platform and the CWRU bearing dataset [ | Regression | ELM, CNN, and autoencoder |
| [ | Electrical equipment | Electric vehicles | Charge estimation of lithium-ion battery state in electric vehicles | - | Bidirectional GRU circuit module, and attention circuit module |
| [ | Electronic and optical equipment | Electrical and electronics | The condition of rotating machinery in university laboratory by using a single-axis piezoelectric accelerometer | Classification | CNN |
| [ | Transport equipment | High-speed railway | Predictive and proactive maintenance for modeling physical degradation and failure in gas-insulated switchgear in high-speed railway | Regression | LSTM-RNN |
| [ | Electrical equipment | Electrical and electronics | RUL estimation of lithium-ion batteries in NASA Ames prognostics data repository [ | Regression | LSTM |
| [ | Machinery | Aircraft manufacturing | Health condition in a horizontal machining center in an aircraft manufacturing cooperation | Regression | An attribute attentional LSTM |
| [ | Electronic and optical equipment | Medical devices and healthcare services | Failure diagnosis for the Vitros immunoassay analyzer in a local hospital in the United Arab Emirates by using IoT sensors | Classification | SVM |
| [ | Electronic and optical equipment | Nuclear power | Condition monitoring of nuclear infrastructure on NASA turbofan dataset [ | Regression | SVM and logistic regression |
| [ | Electronic and optical equipment | Electrical and electronics | RUL estimation on MOSFET thermal overstress aging dataset [ | Regression | SVM |
| [ | Buildings and other structures | Architecture, engineering, construction, and facility management | Data-driven condition monitoring based on building information modeling and IoT to predict the future condition of the mechanical, electrical, and plumbing components | Regression | FNN and SVM |
| [ | Fabricated metal products | Large service management | Failure prediction on Backblaze dataset [ | Classification | Decision tree-based machine learning method |
| [ | Other machinery and equipment | Renewable energy, wind energy | Fault prediction in wind turbines | Classification | RF, decision tree algorithms, DBSC, and statistical process control |
| [ | Electrical equipment | Electronics manufacturing | RUL estimation of the equipment in an industry manufacturer of memory modules (DRAM and SSD) | Regression | Combined statistical process control charts and RF, XGBoost, and LSTM |
| [ | Electrical equipment | Electrical and electronics | PdM and health monitoring on appropriate quality data collected in the form of product measurements or readings from various machines | Classification | Statistical process control and naïve Bayes |
| [ | Motor vehicles | Autonomous vehicles | Hierarchical component-based health monitoring system with fault detection, diagnosis, and prognosis on the CaRINA II autonomous vehicle platform and the CARLA simulator | Classification | Dynamic Bayesian network |
| [ | Other machinery and equipment | Manufacturing | State and failure prediction of rim welding machine in the process of creating vehicle rims from the iron plate in the assembly line using IoT-based sensors | Classification | Naïve Bayes and Markov chain |
| [ | Electrical equipment | Electrical and electronics | Failure prediction in an electrical motor | Classification | Bayesian network |
| [ | Transport equipment, except motor vehicles | Transport | Failure prediction for rail bridges on the rail network in Great Britain | - | Bayesian network |
| [ | Motor vehicles | Automotive | Anomaly detection for off-road vehicle maintenance | Classification | HMM, kNN and isolation forest, and autoencoders |
| [ | Motor vehicles | Automotive | The ecological PdM through condition monitoring of a bus with diesel engines taking temperature, humidity, pollutant emissions (NOx, CO2, HC, and PM), emitted noise, etc. | - | HMM |
| [ | Transport equipment | Energy and sustainability | RUL estimation of the machines on the PHM 2008 dataset [ | Regression | Cluster-based HMM |
| [ | Machinery | Semiconductor manufacturing | Condition monitoring in a semiconductor manufacturing station | Clustering | HMM |
| [ | Transport equipment | Energy and sustainability | RUL estimation using degradation indicators in an airplane engine on NASA turbofan engine dataset [ | Unsupervised | HMM |
| [ | Industrial machinery and equipment | Pumping systems | Fault detection and life cycle cost analysis of pumping systems | Statistics Regression Classification | SVM and HMM |
| [ | Transport equipment | Marine | PdM for cost estimation during the design process of a ship engine room | - | Bayesian probabilistic inferential approach and HMM |
| [ | Other machinery and equipment | Pharmaceutical manufacturing | Real-time health monitoring in an industrial freeze-dryer | Clustering, Classification | DBSC, K-means and GMMs, PCA, one-class SVM, and the local outlier factor |
| [ | Fabricated metal products | Cloud services and data centers | The detection of imminent hard disk drive failures in Backblaze dataset [ | Classification | Apache Spark, which is an in-memory distributed data analysis platform, and RF |
| [ | Electronic and optical equipment | Smart manufacturing | A manufacturing big data ecosystem addressing the issues of big data ingestion, management, and analytics for fault/anomaly detection in IoT-based smart factories | Clustering | The distributed K-means clustering, MapReduce-based distributed PCA-based T-squared, and SPE algorithms |
| [ | Transport equipment | Transport | PdM approach for malfunction evaluation in relation to the kilometers of the train and the periodicity of faults in the Greek Railway Company | Regression | Classification trees J48 and regression trees M5 form algorithms |
| [ | Machinery and equipment | Electronics | Failure prediction of the monitored manufacturing industrial machinery by UCI SECOM dataset [ | Classification | A fusion of data mining and semantics |
| [ | Other machinery and equipment | Textile | Production quality prediction in the textile industry | Regression | Supervisory control and data acquisition (SCADA) architecture to develop a cloud-based analytics module |
| [ | Transport equipment, except motor vehicles | Logistics and parcel delivery | A big data analytics framework for the data-driven prediction of courier package breaks in smart goods transportation systems | Classification | IoT networks |
| [ | - | Vinyl flooring | Quality management in the vinyl flooring industry | - | Big data analytics and optimization |
| [ | Electrical equipment | Industrial robots | Health status degradation assessment using real data of the ABB IRB 6400r industrial robot | Regression | Programmable logic controllers (PLC) |
| [ | Electrical equipment | Metal and metallurgy | Fault detection in the friction stir welding tool | Classification | Best first tree classifier |
| [ | Electrical equipment | Electrical and electronics | Detection of the essence of the unbalanced conditions in the rotary machine in the constructed experimental setup | Classification | SVM |
| [ | Electrical equipment | Building and construction | Failure prediction in HVAC installations at a sports facility in a building automation system in the Paris region using sensors such as vibration, temperature, and energy consumption meters | Regression | LSTM and autoencoders |
| [ | Electrical equipment | Manufacturing | Prediction of gradual degradation of an impeller using the sensors such as vibration, gyroscope/accelerometer, rotational speed, temperature, pressure, ambient pressure, temperature, and humidity on an industrial radial fan | Regression | Linear regression, RFR, and symbolic regression |
| [ | Electrical equipment | Semiconductor | Vibration-related failure prediction on a dataset including machines, errors, maintenance, telemetry, and failures | Regression | Linear regression |
| [ | Electrical equipment | Electrical and electronics | Fault detection in rotating machinery by monitoring and visualizing vibrations using transformed raw data into images through a short-time Fourier transform or Mel-frequency cepstral coefficients spectrogram | Classification | CNNs |
| [ | Fabricated metal products | Nuclear energy | PdM in the research reactor by using core-cooling pump vibration signals | Classification | FNN |
| [ | Electrical equipment | Railways | Fault detection using thermal imaging in rail systems in Turkiye | Classification | Fuzzy |
| [ | Machinery | Railways | Locomotive maintenance in Sri Lanka Railways for the issues of premature axle bearing defects, suspension bearing conditions, diesel engine inspection, compressor inspection, weak thermal insulation detection, dynamic grid resister element inspection, water, air, fuel, and oil pipeline blocks, fuse contractors, resistors, relays, and loose electrical wires | - | Thermal imaging technology |
| [ | Electrical equipment | Medical | PdM for progressive deterioration processes and failure mechanisms of different medical equipment | - | Infrared thermal imaging |
| [ | Electrical equipment | Hydraulics | PdM for evaluating premature failures of hydraulic drive systems in a university laboratory using simulation software AMESim from SIEMENS LMS Imagine.Lab | - | Some numerical simulations using infrared thermography |
| [ | Fabricated metal products, machinery, and equipment | Gearbox manufacturing | PdM through failure prediction and analysis in the gearbox high-speed shaft bearing using temperature and vibrational sensors | Classification | Decision trees |
| [ | Electrical equipment | Automotive | Intelligent PdM control through the collected data from condition monitoring sensors of electrical monorail system | Classification | Rule-base intelligence system |
| [ | Fabricated metal products | Electrical and electronics | Fault detection and remote monitoring system to control the status of professional refrigeration systems | Regression | Planned SVM |
| [ | Electrical equipment | Machine tools manufacturing | Fault prediction in machine tools equipped with various sensors to acquire huge volumes of production data in a typical machining workshop in Wuxi, China | Regression | CNN, LSTM, and deep reinforcement learning (DRL) |
| [ | Fabricated metal products | Steel strip processing | Prediction of the real-time fatigue strength of the component under loading in steel strip processing lines | Regression | Finite element analysis, linear regression |
| [ | - | - | PdM system by a digital intelligent assistant for industry | - | NLP and user feedback about the success of maintenance interventions |
| [ | Electrical equipment | Automotive manufacturing | Prescriptive maintenance for evaluating failure and quality effects in an international manufacturer of gearboxes and engines for the automotive sector | - | Data management, predictive data analytic toolbox, recommender and decision support dashboard, and semantic-based learning and reasoning |
| [ | Fabricated metal products | Chemical industry | The prediction of failure time and probability of a pump | Classification | Ensembles of SVMs |
| [ | Electrical equipment | Manufacturing | The prediction of potential equipment failure, expected failure time, and expected repair time and providing the appropriate action for production planning and control in future factories | Reinforcement Learning | RL |
| [ | Machinery | Manufacturing | RUL estimation in a machine park consisting of 100 machines | Reinforcement Learning | DRL |
| [ | Machinery | Rail transport | The optimal maintenance strategy jointly incorporates the effect of aging and degradation for locomotive wheelsets | - | Reliability analysis, sensitivity analysis, and |
| [ | Transport equipment | Aviation | Discrete-event simulation framework for post-prognostic decision for aircraft maintenance using tire pressure indication system for an Airbus A320 | - | The technological maturity of an underlying PHM system |
| [ | Electrical equipment | Manufacturing | Anomaly detection in all the equipment in a global manufacturing system | Unsupervised | Autoencoder-based deep learning technique |
| [ | Machinery | Manufacturing | Failure prediction using different sensors such as temperature, rotation speed, vibration, and humidity in a ball-bearing automatic line | Regression | Autoregressive Integrated moving average model (ARIMA), ARIMA-LSTMs |
| [ | Machinery | Industrial robotics manufacturing | Failure prediction for monitoring | Regression | NN, RF, logistic regression, SVM, and gradient-boosted tree |
| [ | Machinery | Air conditioning manufacturing | Failure prediction in air-conditioning systems | Classification | Centroid distance weighted federated averaging algorithm |
| [ | Electronic and optical equipment | Industry 4.0 | Blockchain framework for PdM in Industry 4.0 | Classification | Fuzzy logic, blockchain, case-based reasoning, and KNN |
| [ | Electrical equipment | Home energy management | Failure prediction of the applications in a home energy management system | Classification | IoT sensors |
| [ | Electrical equipment | Electric vehicles | Prediction of starter battery failure times from a fleet of vehicles | - | Maximum likelihood approach |
| [ | Electrical equipment Machinery | Hydraulics | Energy-based maintenance for lubricant condition monitoring in a rubber-mixing hydraulic control system | Regression | SVM and RF |
| [ | Machinery | Steel manufacturing | RUL estimation in hot rolling milling machines regarding segment surface temperatures and hydraulic force measurements | - | Maximum likelihood approach |
| [ | Fabricated metal products | Robotics manufacturing | Failure prediction in a discrete multi-robot mobile assembling line using sensors such as energy analyzer modules, temperature, vibration, corrosion, and humidity | Classification | FNN |
| [ | Machinery and equipment | Electrical and electronics | PHM-based PdM for the degradation estimation of gear motor assembly in mechanical power transmission | - | Cloud computing and the multitenancy principle |
| [ | Electrical equipment | Electrical and electronics | The detection of changes in the operating conditions and abrupt faults in the platform composed of an asynchronous motor and a gearbox made of two pulleys in the university laboratory | - | Edge and cloud analytics |
| [ | - | Manufacturing | Product lifecycle management by connecting the industrial unit floor with design and manufacturing engineers | - | A predictive analytics software platform |
Summary of the studies in
| Class of Repair and Maintenance | Study Number | Industry | Study Number | PdM Task | Study Number | Problem Type/ | Study Number |
|---|---|---|---|---|---|---|---|
| Electrical equipment | 30 | Electrical and electronics; electronics | 13 | Failure prediction | 22 | Classification | 33 |
| Machinery | 10 | Manufacturing; manufacturing of automotive manufacturing; aircraft semiconductor; machine tools; gearbox; smart; pharmaceutical; electronics; steel; robotics; industrial robotics; air conditioning; industrial robots; Industry 4.0 | 22 | RUL estimation; | 11 | Regression | 26 |
| Electronic and optical equipment | 9 | Transport; automotive; high-speed railway; autonomous vehicles; railways; rail transport | 10 | Fault detection | 12 | Clustering | 3 |
| Fabricated metal products | 8 | Metals and plastics; metal and metallurgy | 5 | Condition monitoring; | 9 | Reinforcement Learning | 3 |
| Transport equipment | 6 | Energy and sustainability; | 5 | Anomaly detection | 4 | Unsupervised Learning | 2 |
| Other machinery and equipment | 5 | Information technology; | 2 | Production quality prediction | 1 | The others | 16 |
| Motor vehicles | 3 | Electric vehicle battery technology | 2 | Product lifecycle management | 1 | ||
| Machinery and equipment | 3 | Medical; medical devices and healthcare services | 2 | Component fatigue strength prediction | 1 | ||
| Transport equipment, except motor vehicles | 2 | Building and construction; architecture; engineering; construction; facility management | 2 | PdM system by a digital intelligent assistant | 1 | ||
| Computers and communication equipment | 1 | Hydraulics | 2 | Blockchain framework for PdM | 1 | ||
| Industrial machinery and equipment | 1 | Large service management | 1 | A big data analytics framework | 1 | ||
| Buildings and other structures | 1 | Infrastructure; pumping systems; marine; textile; logistics and parcel delivery; steel strip processing; chemical industry; aviation; home energy management | 9 | Post-prognostic decision | 1 |
Comparison of AI methods in PdM systems.
| Techniques | Accuracy | Cost-Effectiveness | Scalability |
|---|---|---|---|
| Deep Learning | High | Moderate | High |
| Machine Learning | Moderate | High | Moderate |
Comparison of AI-based PdM systems with traditional methods.
| Methods | Accuracy | Cost-Effectiveness | Scalability |
|---|---|---|---|
| AI-based | High | Moderate | High |
| Traditional | Moderate | High | Low |
Summary of different transparency and explainability approaches used in AI-based PdM.
| Ref. | The Approach | Application |
|---|---|---|
| [ | SHAP | PdM in a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter |
| [ | SHAP | PdM in a coal crusher operating presented in [ |
| [ | LIME, SHAP, and ELI5 | Solar photovoltaic energy forecasting by GEFCOM dataset [ |
| [ | Integrated gradients, SHAP, and SmoothGrad | Anomaly detection in press machine data of a production line |
| [ | SHAP and LIME. | RUL estimation of hard disk drive in the Backblaze dataset [ |
| [ | SHAP | RUL estimation of the engines on the NASA turbofan engine dataset [ |
| [ | Integrated gradients | PdM to reveal the most sensitive gearbox operations in the MW load range in a petrochemical plant to a specific abnormality |
| [ | SHAP | RUL estimation of NASA turbofan engine dataset [ |
| [ | LIME and NLP | Maintenance work orders |
| [ | LRP | Bearing health condition estimation on the NASA bearing dataset [ |
| [ | Knowledge-based system including domain ontologies and semantic web rule language rules | The detection of future machinery failures as well as the prediction of their time of occurrence in semiconductor manufacturing process by the UCI SECOM dataset [ |
| [ | Rule-based expert system | PdM of a real hybrid bus |
| [ | Real type-2 fuzzy-based XAI | PdM within the water pumping industry |
| [ | Rule-based model called logic language model | RUL estimation of the engines on the NASA turbofan engine dataset [ |
| [ | QARM algorithm | RUL prediction of a drilling machine of an automotive manufacturer |
| [ | A data-driven sensitivity analysis | The prediction of the future reliability of components in a large gas distribution network |
| [ | Bagged decision trees | A synthetic dataset that reflects real predictive maintenance data encountered in the industry |
| [ | Gradient boosting decision tree | The prediction machine errors or tool failures on Microsoft Azure dataset [ |
| [ | A premier transparent, interpretable, and self-explainable automated machine learning software, including methods like random forest and gradient boosting | Manufacturing quality prediction in real-life environment |
| [ | A virtual knowledge graph-based approach | PdM in the hydraulic systems |
| [ | Attention and LSTM-GAN | PdM to reduce maintenance costs and downtime of machines in the intelligent manufacturing system |
| [ | Attention and bidirectional LSTM | RUL estimation on the NASA turbofan engine dataset [ |
| [ | Bidirectional self-attention gated recurrent unit | The prediction of the health index on the NASA rolling bearing dataset from IEEE PHM challenge [ |
| [ | The attention mechanism | RUL estimation on the NASA turbofan engine dataset [ |
| [ | The attention mechanism | Structural Health Monitoring |
| [ | The multi-layer, multi-source attention distribution | The fault detection and recognition on the general data hierarchy of AUV |
| [ | Attention and LSTM | RUL estimation on the NASA turbofan engine dataset [ |
| [ | Feature attribution and counterfactual generation | Fault diagnosis in water injection pump for production stimulation in offshore oil wells, offshore natural gas treatment plant |
| [ | PCA/Kernel PCA/KNN-PCA, | RUL estimation of the NASA turbofan engine dataset [ |
| [ | Pobability density function, Fourier transform, spectral kurtosis, autoencoder and variational autoencoder, and K-means clustering | Gearbox and bearing health assessment in wind turbine system |
| [ | Unsupervised feature selection, adapting relevance metrics with the dynamic time-warping algorithm | Health indicators for a rotating machine |
| [ | Temporal fusion separable convolutional network, a hierarchical latent space variational auto-encoder, and a regressor consisting of a linear layer and a sigmoid activation function | RUL estimation on the NASA turbofan engine dataset [ |
| [ | A blockchain-based architecture that achieves trustworthy federated learning | A service |
| [ | Balanced K-star | PdM in an IoT-based manufacturing system |
| [ | HMM and reinforcement learning | RUL estimation of the engines on the NASA turbofan engine dataset [ |
References
1. Dalzochio, J.; Kunst, R.; Pignaton, E.; Binotto, A.; Sanyal, S.; Favilla, J.; Barbosa, J. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Ind.; 2020; 123, 103298. [DOI: https://dx.doi.org/10.1016/j.compind.2020.103298]
2. Cho, H. Uncertainty Management in Prognosis of Electric Vehicle Energy System. Ph.D. Dissertation; School of Electrical and Computer Engineering, Georgia Institute of Technology: Atlanta, GA, USA, 2018.
3. Abbasi, T.; Lim, K.H.; Yam, K.S. Predictive maintenance of oil and gas equipment using recurrent neural network. Proceedings of the Iop Conference Series: Materials Science and Engineering; Jakarta, Indonesia, 21–22 November 2019; 012067.
4. Zhang, W.; Yang, D.; Wang, H. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst. J.; 2019; 13, pp. 2213-2227. [DOI: https://dx.doi.org/10.1109/JSYST.2019.2905565]
5. Huang, M.; Liu, Z.; Tao, Y. Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul. Model. Pract. Theory; 2020; 102, 101981. [DOI: https://dx.doi.org/10.1016/j.simpat.2019.101981]
6. Arena, F.; Collotta, M.; Luca, L.; Ruggieri, M.; Termine, F.G. Predictive maintenance in the automotive sector: A literature review. Math. Comput. Appl.; 2021; 27, 2. [DOI: https://dx.doi.org/10.3390/mca27010002]
7. Zhang, S.; Liu, C.; Su, S.; Han, Y.; Li, X. A feature extraction method for predictive maintenance with time-lagged correlation–based curve-registration model. Int. J. Netw. Manag.; 2018; 28, e2025. [DOI: https://dx.doi.org/10.1002/nem.2025]
8. Harris, A.; Yellen, M. Decision-Making with Machine Prediction: Evidence from Predictive Maintenance in Trucking. 2024; Available online: https://adamharris380.github.io/files/HarrisYellen-JMP.pdf (accessed on 19 January 2024).
9. Ahmad, A.A.; Alshurideh, M. Digital Twin in Facility Management Operational Decision Making and Predictive Maintenance. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics; Cairo, Egypt, 20–22 November 2022; pp. 437-448.
10. Achouch, M.; Dimitrova, M.; Ziane, K.; Sattarpanah Karganroudi, S.; Dhouib, R.; Ibrahim, H.; Adda, M. On predictive maintenance in industry 4.0: Overview, models, and challenges. Appl. Sci.; 2022; 12, 8081. [DOI: https://dx.doi.org/10.3390/app12168081]
11. Khatri, M.R. Integration of natural language processing, self-service platforms, predictive maintenance, and prescriptive analytics for cost reduction, personalization, and real-time insights customer service and operational efficiency. Int. J. Inf. Cybersecur.; 2023; 7, pp. 1-30.
12. Vulpio, A.; Oliani, S.; Suman, A.; Zanini, N.; Saccenti, P. A Mechanistic Model for the Predictive Maintenance of Heavy-Duty Centrifugal Fans Operating With Dust-Laden Flows. J. Eng. Gas Turbines Power; 2023; 145, 011007. [DOI: https://dx.doi.org/10.1115/1.4055709]
13. Xia, L.; Liang, Y.; Leng, J.; Zheng, P. Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network. Reliab. Eng. Syst. Saf.; 2023; 232, 109068. [DOI: https://dx.doi.org/10.1016/j.ress.2022.109068]
14. Gao, Q.; Yang, Y.; Kang, Q.; Tian, Z.; Song, Y. EEG-based emotion recognition with feature fusion networks. Int. J. Mach. Learn. Cybern.; 2022; 13, pp. 421-429. [DOI: https://dx.doi.org/10.1007/s13042-021-01414-5]
15. Ouadah, A.; Zemmouchi-Ghomari, L.; Salhi, N. Selecting an appropriate supervised machine learning algorithm for predictive maintenance. Int. J. Adv. Manuf. Technol.; 2022; 119, pp. 4277-4301. [DOI: https://dx.doi.org/10.1007/s00170-021-08551-9]
16. Jiang, Y.; Dai, P.; Fang, P.; Zhong, R.Y.; Cao, X. Electrical-STGCN: An electrical spatio-temporal graph convolutional network for intelligent predictive maintenance. IEEE Trans. Ind. Inform.; 2022; 18, pp. 8509-8518. [DOI: https://dx.doi.org/10.1109/TII.2022.3143148]
17. Del Buono, F.; Calabrese, F.; Baraldi, A.; Paganelli, M.; Regattieri, A. Data-driven predictive maintenance in evolving environments: A comparison between machine learning and deep learning for novelty detection. Proceedings of the International Conference on Sustainable Design and Manufacturing; Split, Croatia, 15–17 September 2021; pp. 109-119.
18. Zhao, J.; Gao, C.; Tang, T. A review of sustainable maintenance strategies for single component and multicomponent equipment. Sustainability; 2022; 14, 2992. [DOI: https://dx.doi.org/10.3390/su14052992]
19. Lee, H.; Kang, D.H.; Jeong, S.C. A Study on Industrial Artificial Intelligence-Based Edge Analysis for Machining Facilities. Emotional Artificial Intelligence and Metaverse; Springer: New York, NY, USA, 2022; pp. 55-69.
20. Sanzana, M.R.; Maul, T.; Wong, J.Y.; Abdulrazic, M.O.M.; Yip, C.-C. Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning. Autom. Constr.; 2022; 141, 104445. [DOI: https://dx.doi.org/10.1016/j.autcon.2022.104445]
21. Lv, J.; Li, X.; Sun, Y.; Zheng, Y.; Bao, J. A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools. Robot. Comput. Integr. Manuf.; 2023; 80, 102489. [DOI: https://dx.doi.org/10.1016/j.rcim.2022.102489]
22. Ren, Y. Optimizing predictive maintenance with machine learning for reliability improvement. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng.; 2021; 7, 030801. [DOI: https://dx.doi.org/10.1115/1.4049525]
23. Cardoso, D.; Ferreira, L. Application of predictive maintenance concepts using artificial intelligence tools. Appl. Sci.; 2020; 11, 18. [DOI: https://dx.doi.org/10.3390/app11010018]
24. Ye, Y.; Yong, Z.; Han, D. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Autom. Sin.; 2020; 46, pp. 2013-2030.
25. Paolanti, M.; Romeo, L.; Felicetti, A.; Mancini, A.; Frontoni, E.; Loncarski, J. Machine learning approach for predictive maintenance in industry 4.0. Proceedings of the 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA); Oulu, Finland, 2–4 July 2018; pp. 1-6.
26. Bordegoni, M.; Ferrise, F. Exploring the intersection of metaverse, digital twins, and artificial intelligence in training and maintenance. J. Comput. Inf. Sci. Eng.; 2023; 23, 060806. [DOI: https://dx.doi.org/10.1115/1.4062455]
27. Carvalho, T.P.; Soares, F.A.; Vita, R.; Francisco, R.d.P.; Basto, J.P.; Alcalá, S.G. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng.; 2019; 137, 106024. [DOI: https://dx.doi.org/10.1016/j.cie.2019.106024]
28. Traini, E.; Bruno, G.; D’antonio, G.; Lombardi, F. Machine learning framework for predictive maintenance in milling. IFAC-Pap.; 2019; 52, pp. 177-182. [DOI: https://dx.doi.org/10.1016/j.ifacol.2019.11.172]
29. Çınar, Z.M.; Abdussalam Nuhu, A.; Zeeshan, Q.; Korhan, O.; Asmael, M.; Safaei, B. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability; 2020; 12, 8211. [DOI: https://dx.doi.org/10.3390/su12198211]
30. Aremu, O.O.; Palau, A.S.; Parlikad, A.K.; Hyland-Wood, D.; McAree, P.R. Structuring data for intelligent predictive maintenance in asset management. IFAC-Pap.; 2018; 51, pp. 514-519. [DOI: https://dx.doi.org/10.1016/j.ifacol.2018.08.370]
31. Jimenez, V.J.; Bouhmala, N.; Gausdal, A.H. Developing a predictive maintenance model for vessel machinery. J. Ocean Eng. Sci.; 2020; 5, pp. 358-386. [DOI: https://dx.doi.org/10.1016/j.joes.2020.03.003]
32. Shukla, B.; Fan, I.-S.; Jennions, I. Opportunities for explainable artificial intelligence in aerospace predictive maintenance. Proceedings of the PHM Society European Conference; Turin, Italy, 1–3 July 2020; 11.
33. Theissler, A.; Pérez-Velázquez, J.; Kettelgerdes, M.; Elger, G. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliab. Eng. Syst. Saf.; 2021; 215, 107864. [DOI: https://dx.doi.org/10.1016/j.ress.2021.107864]
34. Ayvaz, S.; Alpay, K. Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Syst. Appl.; 2021; 173, 114598. [DOI: https://dx.doi.org/10.1016/j.eswa.2021.114598]
35. Liu, Z.; Meyendorf, N.; Mrad, N. The role of data fusion in predictive maintenance using digital twin. Proceedings of the AIP Conference Proceedings; Depok, Indonesia, 30–31 October 2018.
36. Massaro, A.; Selicato, S.; Galiano, A. Predictive maintenance of bus fleet by intelligent smart electronic board implementing artificial intelligence. IoT; 2020; 1, 12. [DOI: https://dx.doi.org/10.3390/iot1020012]
37. Hosamo, H.H.; Svennevig, P.R.; Svidt, K.; Han, D.; Nielsen, H.K. A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics. Energy Build.; 2022; 261, 111988. [DOI: https://dx.doi.org/10.1016/j.enbuild.2022.111988]
38. Cakir, M.; Guvenc, M.A.; Mistikoglu, S. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Comput. Ind. Eng.; 2021; 151, 106948. [DOI: https://dx.doi.org/10.1016/j.cie.2020.106948]
39. De Benedetti, M.; Leonardi, F.; Messina, F.; Santoro, C.; Vasilakos, A. Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing; 2018; 310, pp. 59-68. [DOI: https://dx.doi.org/10.1016/j.neucom.2018.05.017]
40. Lee, S.M.; Lee, D.; Kim, Y.S. The quality management ecosystem for predictive maintenance in the Industry 4.0 era. Int. J. Qual. Innov.; 2019; 5, pp. 1-11. [DOI: https://dx.doi.org/10.1186/s40887-019-0029-5]
41. Pech, M.; Vrchota, J.; Bednář, J. Predictive maintenance and intelligent sensors in smart factory. Sensors; 2021; 21, 1470. [DOI: https://dx.doi.org/10.3390/s21041470] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33672479]
42. Mourtzis, D.; Tsoubou, S.; Angelopoulos, J. Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance. Electronics; 2023; 12, 1999. [DOI: https://dx.doi.org/10.3390/electronics12091999]
43. de Oliveira, R.O.; Coppola, M.; Vermesan, O. AI in Food and Beverage Industry. Artificial Intelligence for Digitising Industry–Applications; River Publishers: Roma, Italy, 2022; pp. 251-259.
44. AI, H. High-Level Expert Group on Artificial Intelligence; European Commission: Brussels, Belgium, 2019; 6.
45. Pashami, S.; Nowaczyk, S.; Fan, Y.; Jakubowski, J.; Paiva, N.; Davari, N.; Bobek, S.; Jamshidi, S.; Sarmadi, H.; Alabdallah, A. Explainable Predictive Maintenance. arXiv; 2023; arXiv: 2306.05120
46. Zonta, T.; Da Costa, C.A.; da Rosa Righi, R.; de Lima, M.J.; da Trindade, E.S.; Li, G.P. Predictive maintenance in the Industry 4.0: A systematic literature review. Comput. Ind. Eng.; 2020; 150, 106889. [DOI: https://dx.doi.org/10.1016/j.cie.2020.106889]
47. Shin, W.; Han, J.; Rhee, W. AI-assistance for predictive maintenance of renewable energy systems. Energy; 2021; 221, 119775. [DOI: https://dx.doi.org/10.1016/j.energy.2021.119775]
48. Angelopoulos, J.; Mourtzis, D. An intelligent product service system for adaptive maintenance of Engineered-to-Order manufacturing equipment assisted by augmented reality. Appl. Sci.; 2022; 12, 5349. [DOI: https://dx.doi.org/10.3390/app12115349]
49. Wang, J.; Gao, R.X. Innovative smart scheduling and predictive maintenance techniques. Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology; Elsevier: New York, NY, USA, 2022; pp. 181-207.
50. Lalik, K.; Wątorek, F. Predictive maintenance neural control algorithm for defect detection of the power plants rotating machines using augmented reality goggles. Energies; 2021; 14, 7632. [DOI: https://dx.doi.org/10.3390/en14227632]
51. Namuduri, S.; Narayanan, B.N.; Davuluru, V.S.P.; Burton, L.; Bhansali, S. Deep learning methods for sensor based predictive maintenance and future perspectives for electrochemical sensors. J. Electrochem. Soc.; 2020; 167, 037552. [DOI: https://dx.doi.org/10.1149/1945-7111/ab67a8]
52. Divya, D.; Marath, B.; Santosh Kumar, M. Review of fault detection techniques for predictive maintenance. J. Qual. Maint. Eng.; 2023; 29, pp. 420-441. [DOI: https://dx.doi.org/10.1108/JQME-10-2020-0107]
53. Gianoglio, C.; Ragusa, E.; Gastaldo, P.; Gallesi, F.; Guastavino, F. Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks. Energies; 2021; 14, 4711. [DOI: https://dx.doi.org/10.3390/en14154711]
54. Zonta, T.; da Costa, C.A.; Zeiser, F.A.; de Oliveira Ramos, G.; Kunst, R.; da Rosa Righi, R. A predictive maintenance model for optimizing production schedule using deep neural networks. J. Manuf. Syst.; 2022; 62, pp. 450-462. [DOI: https://dx.doi.org/10.1016/j.jmsy.2021.12.013]
55. De Santo, A.; Ferraro, A.; Galli, A.; Moscato, V.; Sperlì, G. Evaluating time series encoding techniques for predictive maintenance. Expert Syst. Appl.; 2022; 210, 118435. [DOI: https://dx.doi.org/10.1016/j.eswa.2022.118435]
56. Pierleoni, P.; Palma, L.; Belli, A.; Raggiunto, S.; Sabbatini, L. Supervised Regression Learning for Maintenance-related Data. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech); Calabria, Italy, 12–15 September 2022; pp. 1-6.
57. Wu, J.-Y.; Wu, M.; Chen, Z.; Li, X.-L.; Yan, R. Degradation-aware remaining useful life prediction with LSTM autoencoder. IEEE Trans. Instrum. Meas.; 2021; 70, pp. 1-10. [DOI: https://dx.doi.org/10.1109/TIM.2021.3055788]
58. Souza, R.M.; Nascimento, E.G.; Miranda, U.A.; Silva, W.J.; Lepikson, H.A. Deep learning for diagnosis and classification of faults in industrial rotating machinery. Comput. Ind. Eng.; 2021; 153, 107060. [DOI: https://dx.doi.org/10.1016/j.cie.2020.107060]
59. Li, K.; Xiong, M.; Li, F.; Su, L.; Wu, J. A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine. Neurocomputing; 2019; 350, pp. 261-270. [DOI: https://dx.doi.org/10.1016/j.neucom.2019.03.084]
60. Dong, Z.; Ji, X.; Wang, J.; Gu, Y.; Wang, J.; Qi, D. ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State of Charge Estimation. IEEE Trans. Consum. Electron.; 2023; [DOI: https://dx.doi.org/10.1109/TCE.2023.3257201]
61. Balazy, P.; Gut, P.; Knap, P. Neural classifying system for predictive maintenance of rotating devices. IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2022; 012013.
62. Wang, Q.; Bu, S.; He, Z. Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Trans. Ind. Inform.; 2020; 16, pp. 6509-6517. [DOI: https://dx.doi.org/10.1109/TII.2020.2966033]
63. Chen, J.C.; Chen, T.-L.; Liu, W.-J.; Cheng, C.; Li, M.-G. Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Adv. Eng. Inform.; 2021; 50, 101405. [DOI: https://dx.doi.org/10.1016/j.aei.2021.101405]
64. Jiang, Y.; Dai, P.; Fang, P.; Zhong, R.Y.; Zhao, X.; Cao, X. A2-LSTM for predictive maintenance of industrial equipment based on machine learning. Comput. Ind. Eng.; 2022; 172, 108560. [DOI: https://dx.doi.org/10.1016/j.cie.2022.108560]
65. Shamayleh, A.; Awad, M.; Farhat, J. IoT based predictive maintenance management of medical equipment. J. Med. Syst.; 2020; 44, pp. 1-12. [DOI: https://dx.doi.org/10.1007/s10916-020-1534-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32078712]
66. Gohel, H.A.; Upadhyay, H.; Lagos, L.; Cooper, K.; Sanzetenea, A. Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nucl. Eng. Technol.; 2020; 52, pp. 1436-1442. [DOI: https://dx.doi.org/10.1016/j.net.2019.12.029]
67. Li, K.; Zhang, Y.; Song, S.; Zhao, Z.; Wang, L. Study on Life Prediction Method of MOSFET Thermal Environment Experiments Based on Extended Kalman Filter. Man-Machine-Environment System Engineering, Proceedings of the 20th International Conference on MMESE, Beijing, China, 20–23 October 2020; Springer: New Yorik, NY, USA, 2020; pp. 495-503.
68. Cheng, J.C.; Chen, W.; Chen, K.; Wang, Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Autom. Constr.; 2020; 112, 103087. [DOI: https://dx.doi.org/10.1016/j.autcon.2020.103087]
69. Kaparthi, S.; Bumblauskas, D. Designing predictive maintenance systems using decision tree-based machine learning techniques. Int. J. Qual. Reliab. Manag.; 2020; 37, pp. 659-686. [DOI: https://dx.doi.org/10.1108/IJQRM-04-2019-0131]
70. Hsu, J.-Y.; Wang, Y.-F.; Lin, K.-C.; Chen, M.-Y.; Hsu, J.H.-Y. Wind turbine fault diagnosis and predictive maintenance through statistical process control and machine learning. Ieee Access; 2020; 8, pp. 23427-23439. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.2968615]
71. Bezerra, A.; da Silva, K.C.N.; Nascimento, E. Industrial environment: A strategy for preventive maintenance using Machine Learning to predict the useful life of equipment and Statistical Process Control for Continuous Monitoring of Variables. INFOCOMP J. Comput. Sci.; 2023; 22, pp. 1-9. Available online: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3029 (accessed on 19 January 2024).
72. Saidy, C.; Xia, K.; Kircaliali, A.; Harik, R.; Bayoumi, A. The application of statistical quality control methods in predictive maintenance 4.0: An unconventional use of statistical process control (SPC) charts in health monitoring and predictive analytics. Advances in Asset Management and Condition Monitoring: COMADEM 2019; Springer: New York, NY, USA, 2020; pp. 1051-1061.
73. Gomes, I.P.; Wolf, D.F. Health monitoring system for autonomous vehicles using dynamic Bayesian networks for diagnosis and prognosis. J. Intell. Robot. Syst.; 2021; 101, pp. 1-21. [DOI: https://dx.doi.org/10.1007/s10846-020-01293-y]
74. Devare, M. Predictive Maintenance in Industry 4.0. Computational Intelligent Security in Wireless Communications; CRC Press: New York, NY, USA, 2023; pp. 79-98.
75. Lakehal, A.; Ramdane, A.; Tachi, F. Probabilistic reasoning for improving the predictive maintenance of vital electrical machine: Case study. J. Adv. Eng. Comput.; 2018; 2, pp. 9-17. [DOI: https://dx.doi.org/10.25073/jaec.201821.74]
76. Zhang, H.; Marsh, D.W.R. Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. Proc. Inst. Mech. Eng. Part O J. Risk Reliab.; 2018; 232, pp. 505-523. [DOI: https://dx.doi.org/10.1177/1748006X17742765]
77. Perini, L. Predictive Maintenance for off-Road Vehicles Based on Hidden Markov Models and Autoencoders for Trend Anomaly Detection. Ph.D. Thesis; Politecnico di Torino: Torino, Italy, 2019.
78. Simões, A.; Farinha, J.T.; Fonseca, I. Ecological Predictive Maintenance of Diesel Engines. Diesel and Gasoline Engines; IntechOpen: London, UK, 2020; 137. [DOI: https://dx.doi.org/10.5772/intechopen.82599]
79. Wu, Z.; Luo, H.; Yang, Y.; Lv, P.; Zhu, X.; Ji, Y.; Wu, B. K-PdM: KPI-oriented machinery deterioration estimation framework for predictive maintenance using cluster-based hidden Markov model. IEEE Access; 2018; 6, pp. 41676-41687. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2859922]
80. Kinghorst, J.; Geramifard, O.; Luo, M.; Chan, H.-L.; Yong, K.; Folmer, J.; Zou, M.; Vogel-Heuser, B. Hidden Markov model-based predictive maintenance in semiconductor manufacturing: A genetic algorithm approach. Proceedings of the 2017 13th IEEE Conference on Automation Science and Engineering (CASE); Xi’an, China, 20–23 August 2017; pp. 1260-1267.
81. Delmas, A.; Sallak, M.; Schön, W.; Zhao, L. Remaining useful life estimation methods for predictive maintenance models: Defining intervals and strategies for incomplete data. Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability; Manchester, UK, 13–15 June 2018; pp. 48-53.
82. Dutta, N.; Palanisamy, K.; Shanmugam, P.; Subramaniam, U.; Selvam, S. Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model. Processes; 2023; 11, 2157. [DOI: https://dx.doi.org/10.3390/pr11072157]
83. Gualeni, P.; Vairo, T. A prediction tool for maintenance costs estimation during the design process of a ship engine room. J. Ocean Eng. Mar. Energy; 2023; 6, pp. 653-663. [DOI: https://dx.doi.org/10.1007/s40722-023-00288-9]
84. Oliosi, E.; Calzavara, G.; Ferrari, G. On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance. IEEE Sens. J.; 2023; 23, pp. 9620-9639. [DOI: https://dx.doi.org/10.1109/JSEN.2023.3260314]
85. Su, C.-J.; Huang, S.-F. Real-time big data analytics for hard disk drive predictive maintenance. Comput. Electr. Eng.; 2018; 71, pp. 93-101. [DOI: https://dx.doi.org/10.1016/j.compeleceng.2018.07.025]
86. Yu, W.; Dillon, T.; Mostafa, F.; Rahayu, W.; Liu, Y. A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Trans. Ind. Inform.; 2019; 16, pp. 183-192. [DOI: https://dx.doi.org/10.1109/TII.2019.2915846]
87. Kalathas, I.; Papoutsidakis, M. Predictive maintenance using machine learning and data mining: A pioneer method implemented to Greek railways. Designs; 2021; 5, 5. [DOI: https://dx.doi.org/10.3390/designs5010005]
88. Cao, Q.; Samet, A.; Zanni-Merk, C.; de Bertrand de Beuvron, F.; Reich, C. Combining chronicle mining and semantics for predictive maintenance in manufacturing processes. Semant. Web; 2020; 11, pp. 927-948. [DOI: https://dx.doi.org/10.3233/SW-200406]
89. Chang, R.-I.; Lee, C.-Y.; Hung, Y.-H. Cloud-based analytics module for predictive maintenance of the textile manufacturing process. Appl. Sci.; 2021; 11, 9945. [DOI: https://dx.doi.org/10.3390/app11219945]
90. Proto, S.; Di Corso, E.; Apiletti, D.; Cagliero, L.; Cerquitelli, T.; Malnati, G.; Mazzucchi, D. REDTag: A predictive maintenance framework for parcel delivery services. IEEE Access; 2020; 8, pp. 14953-14964. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.2966568]
91. Bajic, B.; Suzic, N.; Moraca, S.; Stefanović, M.; Jovicic, M.; Rikalovic, A. Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective. Sustainability; 2023; 15, 6032. [DOI: https://dx.doi.org/10.3390/su15076032]
92. Izagirre, U.; Andonegui, I.; Landa-Torres, I.; Zurutuza, U. A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines. Robot. Comput. Integr. Manuf.; 2022; 74, 102287. [DOI: https://dx.doi.org/10.1016/j.rcim.2021.102287]
93. Balachandar, K.; Jegadeeshwaran, R.; Gandhikumar, D. Condition monitoring of FSW tool using vibration analysis–A machine learning approach. Mater. Today Proc.; 2020; 27, pp. 2970-2974. [DOI: https://dx.doi.org/10.1016/j.matpr.2020.04.903]
94. Pinheiro, A.A.; Brandao, I.M.; Da Costa, C. Vibration analysis in turbomachines using machine learning techniques. Eur. J. Eng. Technol. Res.; 2019; 4, pp. 12-16.
95. Bouabdallaoui, Y.; Lafhaj, Z.; Yim, P.; Ducoulombier, L.; Bennadji, B. Predictive maintenance in building facilities: A machine learning-based approach. Sensors; 2021; 21, 1044. [DOI: https://dx.doi.org/10.3390/s21041044] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33546418]
96. Zenisek, J.; Holzinger, F.; Affenzeller, M. Machine learning based concept drift detection for predictive maintenance. Comput. Ind. Eng.; 2019; 137, 106031. [DOI: https://dx.doi.org/10.1016/j.cie.2019.106031]
97. King, R.; Curran, K. Predictive Maintenance for Vibration-Related failures in the Semi-Conductor Industry. J. Comput. Eng. Inf. Technol.; 2019; 8, 1.
98. Lee, S.; Yu, H.; Yang, H.; Song, I.; Choi, J.; Yang, J.; Lim, G.; Kim, K.-S.; Choi, B.; Kwon, J. A study on deep learning application of vibration data and visualization of defects for predictive maintenance of gravity acceleration equipment. Appl. Sci.; 2021; 11, 1564. [DOI: https://dx.doi.org/10.3390/app11041564]
99. Haggag, S. Vibration analysis for predictive maintenance and improved reliability of rotating machines in ETRR-2 research reactor. Kerntechnik; 2022; 87, pp. 125-134. [DOI: https://dx.doi.org/10.1515/kern-2020-0036]
100. Karakose, M.; Yaman, O. Complex fuzzy system based predictive maintenance approach in railways. IEEE Trans. Ind. Inform.; 2020; 16, pp. 6023-6032. [DOI: https://dx.doi.org/10.1109/TII.2020.2973231]
101. Pathirathna, K.A.B.; Dhanushka, R.M.; Rathnayake, M.; Hathanguruge, W.; Fernando, G.D. Use of thermal imaging technology for locomotive maintenance in Sri Lanka Railways. Proceedings of the 2018 International Conference on Intelligent Rail Transportation (ICIRT); Singapore, 12–14 December 2018; pp. 1-4.
102. Andritoi, D.; Luca, C.; Corciova, C.; Ciorap, R. The use of thermography as a prediction element in the maintenance of medical equipment. Proceedings of the 6th International Conference on Advancements of Medicine and Health Care through Technology; Cluj-Napoca, Romania, 17–20 October 2018; pp. 73-78.
103. Marinescu, A.-D.; Chiriţă, A.-P.; Cristescu, C.; Safta, C.-A. Numerical Simulation of Thermal Processes Occurring at Testing Hydrostatic Pumps in Cavitation Mode. Hidraulica; 2018; 1, pp. 55-64.
104. Arena, S.; Florian, E.; Zennaro, I.; Orrù, P.F.; Sgarbossa, F. A novel decision support system for managing predictive maintenance strategies based on machine learning approaches. Saf. Sci.; 2022; 146, 105529. [DOI: https://dx.doi.org/10.1016/j.ssci.2021.105529]
105. Kostoláni, M.; Murín, J.; Kozák, Š. Intelligent predictive maintenance control using augmented reality. Proceedings of the 2019 22nd International Conference on Process Control (PC19); Strbske Pleso, Slovakia, 11–14 June 2019; pp. 131-135.
106. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Intelligent predictive maintenance and remote monitoring framework for industrial equipment based on mixed reality. Front. Mech. Eng.; 2020; 6, 578379. [DOI: https://dx.doi.org/10.3389/fmech.2020.578379]
107. Liu, C.; Zhu, H.; Tang, D.; Nie, Q.; Zhou, T.; Wang, L.; Song, Y. Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing. Robot. Comput. Integr. Manuf.; 2022; 77, 102357. [DOI: https://dx.doi.org/10.1016/j.rcim.2022.102357]
108. Deshpande, A.; Buktar, R. Convergence of Augmented Reality, Internet of Things (Iot) and Machine Learning for Visualizing Finite Element Analysis Results and Predictive Maintenance. 2023; Available online: https://ssrn.com/abstract=4559683 (accessed on 19 January 2024). [DOI: https://dx.doi.org/10.2139/ssrn.4559683]
109. Wellsandt, S.; Klein, K.; Hribernik, K.; Lewandowski, M.; Bousdekis, A.; Mentzas, G.; Thoben, K.-D. Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annu. Rev. Control; 2022; 53, pp. 382-390. [DOI: https://dx.doi.org/10.1016/j.arcontrol.2022.04.001]
110. Ansari, F.; Glawar, R.; Nemeth, T. PriMa: A prescriptive maintenance model for cyber-physical production systems. Int. J. Comput. Integr. Manuf.; 2019; 32, pp. 482-503. [DOI: https://dx.doi.org/10.1080/0951192X.2019.1571236]
111. Gordon, C.A.K.; Burnak, B.; Onel, M.; Pistikopoulos, E.N. Data-driven prescriptive maintenance: Failure prediction using ensemble support vector classification for optimal process and maintenance scheduling. Ind. Eng. Chem. Res.; 2020; 59, pp. 19607-19622. [DOI: https://dx.doi.org/10.1021/acs.iecr.0c03241]
112. Elbasheer, M.; Longo, F.; Mirabelli, G.; Padovano, A.; Solina, V.; Talarico, S. Integrated Prescriptive Maintenance and Production Planning: A Machine Learning Approach for the Development of an Autonomous Decision Support Agent. IFAC-PapersOnLine; 2022; 55, pp. 2605-2610. [DOI: https://dx.doi.org/10.1016/j.ifacol.2022.10.102]
113. Goby, N.; Brandt, T.; Neumann, D. Deep reinforcement learning with combinatorial actions spaces: An application to prescriptive maintenance. Comput. Ind. Eng.; 2023; 179, 109165. [DOI: https://dx.doi.org/10.1016/j.cie.2023.109165]
114. Liu, B.; Lin, J.; Zhang, L.; Kumar, U. A dynamic prescriptive maintenance model considering system aging and degradation. IEEE Access; 2019; 7, pp. 94931-94943. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2928587]
115. Meissner, R.; Rahn, A.; Wicke, K. Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making. Reliab. Eng. Syst. Saf.; 2021; 214, 107812. [DOI: https://dx.doi.org/10.1016/j.ress.2021.107812]
116. Yu, W.; Liu, Y.; Dillon, T.; Rahayu, W. Edge computing-assisted IoT framework with an autoencoder for fault detection in manufacturing predictive maintenance. IEEE Trans. Ind. Inform.; 2022; 19, pp. 5701-5710. [DOI: https://dx.doi.org/10.1109/TII.2022.3178732]
117. Cheng, C.; Zhang, B.-K.; Gao, D. A predictive maintenance solution for bearing production line based on edge-cloud cooperation. Proceedings of the 2019 Chinese Automation Congress (CAC); Hangzhou, China, 22–24 November 2019; pp. 5885-5889.
118. Bowden, D.; Marguglio, A.; Morabito, L.; Napione, C.; Panicucci, S.; Nikolakis, N.; Makris, S.; Coppo, G.; Andolina, S.; Macii, A. A Cloud-to-edge Architecture for Predictive Analytics. Proceedings of the EDBT/ICDT Workshops; Lisbon, Portugal, 26 March 2019.
119. Zhang, W.; Lu, Q.; Yu, Q.; Li, Z.; Liu, Y.; Lo, S.K.; Chen, S.; Xu, X.; Zhu, L. Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet Things J.; 2020; 8, pp. 5926-5937. [DOI: https://dx.doi.org/10.1109/JIOT.2020.3032544]
120. Oladapo, K.A.; Adedeji, F.; Nzenwata, U.J.; Quoc, B.P.; Dada, A. Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0. Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications; Springer: New York, NY, USA, 2023; pp. 269-297.
121. Kaul, K.; Singh, P.; Jain, D.; Johri, P.; Pandey, A.K. Monitoring and Controlling of Energy Consumption using IOT-based Predictive Maintenance. Proceedings of the 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART); Moradabad, India, 10–11 December 2021; pp. 587-594.
122. Holmer, O.; Frisk, E.; Krysander, M. Energy-Based Survival Models for Predictive Maintenance. arXiv; 2023; arXiv: 2302.00629[DOI: https://dx.doi.org/10.1016/j.ifacol.2023.10.762]
123. Orošnjak, M.; Brkljač, N.; Šević, D.; Čavić, M.; Oros, D.; Penčić, M. From predictive to energy-based maintenance paradigm: Achieving cleaner production through functional-productiveness. J. Clean. Prod.; 2023; 408, 137177. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.137177]
124. Anagiannis, I.; Nikolakis, N.; Alexopoulos, K. Energy-based prognosis of the remaining useful life of the coating segments in hot rolling mill. Appl. Sci.; 2020; 10, 6827. [DOI: https://dx.doi.org/10.3390/app10196827]
125. Elahi, M.; Afolaranmi, S.O.; Mohammed, W.M.; Martinez Lastra, J.L. Energy-based prognostics for gradual loss of conveyor belt tension in discrete manufacturing systems. Energies; 2022; 15, 4705. [DOI: https://dx.doi.org/10.3390/en15134705]
126. Fila, R.; El Khaili, M.; Mestari, M. Cloud computing for industrial predictive maintenance based on prognostics and health management. Procedia Comput. Sci.; 2020; 177, pp. 631-638. [DOI: https://dx.doi.org/10.1016/j.procs.2020.10.090]
127. Calabrese, F.; Regattieri, A.; Bortolini, M.; Gamberi, M.; Pilati, F. Predictive maintenance: A novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries. Appl. Sci.; 2021; 11, 3380. [DOI: https://dx.doi.org/10.3390/app11083380]
128. Saidy, C.; Valappil, S.P.; Matthews, R.M.; Bayoumi, A. Development of a predictive maintenance 4.0 platform: Enhancing product design and manufacturing. Advances in Asset Management and Condition Monitoring: COMADEM 2019; Springer: New York, NY, USA, 2020; pp. 1039-1049.
129. Razali, M.N.; Jamaluddin, A.F.; Abdul Jalil, R.; Nguyen, T.K. Big data analytics for predictive maintenance in maintenance management. Prop. Manag.; 2020; 38, pp. 513-529. [DOI: https://dx.doi.org/10.1108/PM-12-2019-0070]
130. De Leon, V.; Alcazar, Y.; Villa, J.L. Use of edge computing for predictive maintenance of industrial electric motors. Proceedings of the Applied Computer Sciences in Engineering: 6th Workshop on Engineering Applications, WEA 2019, Proceedings 6; Santa Marta, Colombia, 16–18 October 2019; pp. 523-533.
131. Yin, J.; Luo, X.; Zhu, Y.; Wang, W.; Wang, L.; Huang, C.; Wang, J.H. An edge computing-based predictive evaluation scheme toward geological drilling data using long short-term memory network. Trans. Emerg. Telecommun. Technol.; 2021; 32, e3888. [DOI: https://dx.doi.org/10.1002/ett.3888]
132. Saxena, A.; Goebel, K. Turbofan Engine Degradation Simulation Data Set. NASA Ames Progn. Data Repos.; 2008; Available online: https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/ (accessed on 19 January 2024).
133. Wen, M.-S.; Wang, C.-Y.; Yeh, J.-K.; Chen, C.-C.; Tsai, M.-L.; Ho, M.-Y.; Hung, K.-C.; Hsieh, I. The role of Asprosin in patients with dilated cardiomyopathy. BMC Cardiovasc. Disord.; 2020; 20, pp. 1-8. [DOI: https://dx.doi.org/10.1186/s12872-020-01680-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32894050]
134. Saxena, A.; Goebel, K. Phm08 challenge data set. NASA Ames Prognostics Data Repository; NASA Ames Research Center: Moffett Field, CA, USA, (consulted 2014-02-15) 2008; Available online: http://ti.arc.nasa.gov/project/prognostic-data-repository (accessed on 19 January 2024).
135. Agogino, A.; Goebel, K. Milling data set. NASA Ames Prognostics Data Repository; NASA Ames Research Center: Moffett Field, CA, USA, 2007; Available online: http://ti.arc.nasa.gov/project/prognostic-data-repository (accessed on 19 January 2024).
136. Lee, J.; Qiu, H.; Yu, G.; Lin, J. Bearing Data Set. IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services. 2007; Available online: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/ (accessed on 19 January 2024).
137. Bearing Data Center. Case Western Reserve University Bearing Data; Bearing Data Center: Cleveland, OH, USA, 2013.
138. FEMTO Bearing Data Set; FEMTO-ST Institute: Besançon, France, 2012; IEEE PHM 2012 Data Challenge Available online: https://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/DATA-PHM (accessed on 19 January 2024).
139. Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C. PRONOSTIA: An experimental platform for bearings accelerated degradation tests. Proceedings of the IEEE International Conference on Prognostics and Health Management, PHM’12; Minneapolis, MN, USA, 23–27 September 2012; pp. 1-8.
140. Backblaze. Hard Drive Data and Stats 2019. Available online: https://www.backblaze.com/b2/hard-drive-test-data.html (accessed on 30 December 2023).
141. PAKDD2020 Alibaba AI OPS Competition. 2020; Available online: https://tianchi.aliyun.com/competition/entrance/231775/introduction (accessed on 30 December 2023).
142. Saha, B.; Goebel, K. Battery data set. NASA Ames Prognostics Data Repository; NASA Ames Research Center: Moffett Field, CA, USA, 2007; Available online: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ (accessed on 19 January 2024).
143. Hasib, S.A.; Islam, S.; Chakrabortty, R.K.; Ryan, M.J.; Saha, D.K.; Ahamed, M.H.; Moyeen, S.I.; Das, S.K.; Ali, M.F.; Islam, M.R. A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. IEEE Access; 2021; 9, pp. 86166-86193. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3089032]
144. Celaya, J.; Saxena, A.; Saha, S.; Goebel, K. MOSFET thermal overstress aging data set. NASA Ames Prognostics Data Repository; NASA Ames Research Center: Moffett Field, CA, USA, 2011; Available online: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ (accessed on 19 January 2024).
145. Ribeiro, F. MaFaulDa-Machinery Fault Database; Signals, Multimedia, and Telecommunications Laboratory: Rio de Janeiro, Brazil, 2016.
146. Microsoft Azure. Azure ai Guide for Predictive Maintenance Solutions. 2020; Available online: https://docs.microsoft.com/pt-br/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook#solution-templates-for-predictive-maintenance (accessed on 30 December 2023).
147. Hong, T.; Pinson, P.; Fan, S.; Zareipour, H.; Troccoli, A.; Hyndman, R.J. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. Int. J. Forecast.; 2016; 32, pp. 896-913. [DOI: https://dx.doi.org/10.1016/j.ijforecast.2016.02.001]
148. McCann, M.; Johnston, A. SECOM Dataset UCI Machine Learning Repository. 2008; Available online: https://archive.ics.uci.edu/ml/datasets/secom (accessed on 19 January 2024).
149. UN Statistics Division. International Standard Industrial Classification of All Economic Activities (ISIC); United Nations Publications: New York, NY, USA, 2008.
150. Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion; 2020; 58, pp. 82-115. [DOI: https://dx.doi.org/10.1016/j.inffus.2019.12.012]
151. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell.; 2019; 1, pp. 206-215. [DOI: https://dx.doi.org/10.1038/s42256-019-0048-x]
152. Gilpin, L.H.; Bau, D.; Yuan, B.Z.; Bajwa, A.; Specter, M.; Kagal, L. Explaining explanations: An overview of interpretability of machine learning. Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA); Turin, Italy, 1–3 October 2018; pp. 80-89.
153. Fan, F.-L.; Xiong, J.; Li, M.; Wang, G. On interpretability of artificial neural networks: A survey. IEEE Trans. Radiat. Plasma Med. Sci.; 2021; 5, pp. 741-760. [DOI: https://dx.doi.org/10.1109/TRPMS.2021.3066428]
154. Slack, D.; Hilgard, S.; Jia, E.; Singh, S.; Lakkaraju, H. Fooling lime and shap: Adversarial attacks on post hoc explanation methods. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society; New York, NY, USA, 7–8 February 2020; pp. 180-186.
155. Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why should i trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; New York, NY, USA, 13–17 August 2016; pp. 1135-1144.
156. Lundberg, S.M.; Erion, G.; Chen, H.; DeGrave, A.; Prutkin, J.M.; Nair, B.; Katz, R.; Himmelfarb, J.; Bansal, N.; Lee, S.-I. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell.; 2020; 2, pp. 56-67. [DOI: https://dx.doi.org/10.1038/s42256-019-0138-9]
157. Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas, NV, USA, 27–30 June 2016; pp. 2921-2929.
158. Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.-R.; Samek, W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE; 2015; 10, e0130140. [DOI: https://dx.doi.org/10.1371/journal.pone.0130140]
159. Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision; Venice, Italy, 27–29 October 2017; pp. 618-626.
160. Nor, A.K.M.; Pedapati, S.R.; Muhammad, M.; Leiva, V. Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic reviews and meta-analyses. Sensors; 2021; 21, 8020. [DOI: https://dx.doi.org/10.3390/s21238020]
161. Došilović, F.K.; Brčić, M.; Hlupić, N. Explainable artificial intelligence: A survey. Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); Opatija, Croatia, 21–25 May 2018; pp. 0210-0215.
162. Carvalho, D.V.; Pereira, E.M.; Cardoso, J.S. Machine learning interpretability: A survey on methods and metrics. Electronics; 2019; 8, 832. [DOI: https://dx.doi.org/10.3390/electronics8080832]
163. Molnar, C. Interpretable Machine Learning. 2020; Available online: https://christophm.github.io/interpretable-ml-book/ (accessed on 19 January 2024).
164. Izza, Y.; Ignatiev, A.; Marques-Silva, J. On explaining decision trees. arXiv; 2020; arXiv: 2010.11034
165. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst.; 2017; 30, pp. 6000-6010.
166. Alhassan, A.M.; Zainon, W.M.N.W. Review of feature selection, dimensionality reduction and classification for chronic disease diagnosis. IEEE Access; 2021; 9, pp. 87310-87317. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3088613]
167. Blum, A.L.; Langley, P. Selection of relevant features and examples in machine learning. Artif. Intell.; 1997; 97, pp. 245-271. [DOI: https://dx.doi.org/10.1016/S0004-3702(97)00063-5]
168. Ferreira, A.J.; Figueiredo, M.A. Efficient feature selection filters for high-dimensional data. Pattern Recognit. Lett.; 2012; 33, pp. 1794-1804. [DOI: https://dx.doi.org/10.1016/j.patrec.2012.05.019]
169. Cernuda, C. On the relevance of preprocessing in predictive maintenance for dynamic systems. Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications; Springer: New York, NY, USA, 2019; pp. 53-93.
170. Ojala, J. On Analysis of the Predictive Maintenance of Railway Points Processes and Possibilities. Master’s Thesis; School of Science, Aalto University: Espoo, Finland, 14 May 2018.
171. Mahalle, P.N.; Hujare, P.P.; Shinde, G.R. Predictive Analytics for Mechanical Engineering: A Beginners Guide; Springer Nature: New York, NY, USA, 2023.
172. Baaj, I. Explainability of Possibilistic and Fuzzy Rule-Based Systems. Ph.D. Thesis; Sorbonne Université: Paris, France, 2022.
173. Duan, H.; Ökten, G. Derivative-based Shapley value for global sensitivity analysis and machine learning explainability. arXiv; 2023; arXiv: 2303.15183
174. Hermansa, M.; Kozielski, M.; Michalak, M.; Szczyrba, K.; Wróbel, Ł.; Sikora, M. Sensor-based predictive maintenance with reduction of false alarms—A case study in heavy industry. Sensors; 2021; 22, 226. [DOI: https://dx.doi.org/10.3390/s22010226]
175. Kozielski, M. Contextual Explanations for Decision Support in Predictive Maintenance. Appl. Sci.; 2023; 13, 10068. [DOI: https://dx.doi.org/10.3390/app131810068]
176. Kuzlu, M.; Cali, U.; Sharma, V.; Güler, Ö. Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools. IEEE Access; 2020; 8, pp. 187814-187823. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3031477]
177. Serradilla, O.; Zugasti, E.; Ramirez de Okariz, J.; Rodriguez, J.; Zurutuza, U. Adaptable and explainable predictive maintenance: Semi-supervised deep learning for anomaly detection and diagnosis in press machine data. Appl. Sci.; 2021; 11, 7376. [DOI: https://dx.doi.org/10.3390/app11167376]
178. Ferraro, A.; Galli, A.; Moscato, V.; Sperlì, G. Evaluating explainable artificial intelligence tools for hard disk drive predictive maintenance. Artif. Intell. Rev.; 2023; 56, pp. 7279-7314. [DOI: https://dx.doi.org/10.1007/s10462-022-10354-7]
179. Youness, G.; Aalah, A. An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction. Aerospace; 2023; 10, 474. [DOI: https://dx.doi.org/10.3390/aerospace10050474]
180. Hajgató, G.; Wéber, R.; Szilágyi, B.; Tóthpál, B.; Gyires-Tóth, B.; Hős, C. PredMaX: Predictive maintenance with explainable deep convolutional autoencoders. Adv. Eng. Inform.; 2022; 54, 101778. [DOI: https://dx.doi.org/10.1016/j.aei.2022.101778]
181. Hong, C.W.; Lee, C.; Lee, K.; Ko, M.-S.; Kim, D.E.; Hur, K. Remaining useful life prognosis for turbofan engine using explainable deep neural networks with dimensionality reduction. Sensors; 2020; 20, 6626. [DOI: https://dx.doi.org/10.3390/s20226626] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33228051]
182. Usuga-Cadavid, J.P.; Lamouri, S.; Grabot, B.; Fortin, A. Using deep learning to value free-form text data for predictive maintenance. Int. J. Prod. Res.; 2022; 60, pp. 4548-4575. [DOI: https://dx.doi.org/10.1080/00207543.2021.1951868]
183. Wu, H.; Huang, A.; Sutherland, J.W. Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance. Int. J. Adv. Manuf. Technol.; 2022; 118, pp. 963-978. [DOI: https://dx.doi.org/10.1007/s00170-021-07911-9]
184. Cao, Q.; Zanni-Merk, C.; Samet, A.; Reich, C.; De Beuvron, F.D.B.; Beckmann, A.; Giannetti, C. KSPMI: A knowledge-based system for predictive maintenance in industry 4.0. Robot. Comput. Integr. Manuf.; 2022; 74, 102281. [DOI: https://dx.doi.org/10.1016/j.rcim.2021.102281]
185. Chen, W. A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus. Ph.D. Thesis; Université d’Ottawa/University of Ottawa: Ottawa, ON, Canada, 2020.
186. Upasane, S.J.; Hagras, H.; Anisi, M.H.; Savill, S.; Taylor, I.; Manousakis, K. A Type-2 Fuzzy Based Explainable AI System for Predictive Maintenance within the Water Pumping Industry. IEEE Transactions on Artificial Intelligence; IEEE: New York, NY, USA, 2023.
187. De Bernardi, G.; Narteni, S.; Cambiaso, E.; Mongelli, M. Rule-based out-of-distribution detection. arXiv; 2023; arXiv: 2303.01860[DOI: https://dx.doi.org/10.1109/TAI.2023.3323923]
188. Christou, I.T.; Kefalakis, N.; Zalonis, A.; Soldatos, J. Predictive and explainable machine learning for industrial internet of things applications. Proceedings of the 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS); Marina del Rey, CA, USA, 25–27 May 2020; pp. 213-218.
189. Betz, W.; Papaioannou, I.; Zeh, T.; Hesping, D.; Krauss, T.; Straub, D. Data-Driven Predictive Maintenance for Gas Distribution Networks. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng.; 2022; 8, 04022016. [DOI: https://dx.doi.org/10.1061/AJRUA6.0001237]
190. Matzka, S. Explainable artificial intelligence for predictive maintenance applications. Proceedings of the 2020 Third International Conference on Artificial Intelligence for Industries (ai4i); Irvine, CA, USA, 21–23 September 2020; pp. 69-74.
191. Hrnjica, B.; Softic, S. Explainable AI in manufacturing: A predictive maintenance case study. Proceedings of the IFIP International Conference on Advances in Production Management Systems; Novi Sad, Serbia, 30 August–3 September 2020; pp. 66-73.
192. Garouani, M.; Ahmad, A.; Bouneffa, M.; Hamlich, M.; Bourguin, G.; Lewandowski, A. Towards big industrial data mining through explainable automated machine learning. Int. J. Adv. Manuf. Technol.; 2022; 120, pp. 1169-1188. [DOI: https://dx.doi.org/10.1007/s00170-022-08761-9]
193. Yan, W.; Shi, Y.; Ji, Z.; Sui, Y.; Tian, Z.; Wang, W.; Cao, Q. Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph. Eng. Appl. Artif. Intell.; 2023; 126, 106798. [DOI: https://dx.doi.org/10.1016/j.engappai.2023.106798]
194. Liu, C.; Tang, D.; Zhu, H.; Nie, Q. A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access; 2021; 9, pp. 49557-49575. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3069256]
195. Shah, S.R.B.; Chadha, G.S.; Schwung, A.; Ding, S.X. A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional lstm. Intell. Syst. Appl.; 2021; 10, 200049. [DOI: https://dx.doi.org/10.1016/j.iswa.2021.200049]
196. Li, J.; Li, S.; Ding, Z.; Zheng, A.; Ye, X. Bidirectional self-Attention Gated Recurrent Unit for Health Index Prediction of Rolling Bearings. Proceedings of the 2023 42nd Chinese Control Conference (CCC); Tianjin, China, 24–26 July 2023; pp. 6656-6662.
197. Behizadi, H.R. Application of Attention Mechanism in Deep Neural Network Architecture for System Failure Prognostics. Ph.D. Thesis; Concordia University: Montreal, QC, Canada, 2023.
198. Yu, Y.; Chen, H. Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration. arXiv; 2023; arXiv: 2308.11644
199. Xia, S.; Zhou, X.; Shi, H.; Li, S.; Xu, C. A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV. Ocean Eng.; 2022; 266, 112595. [DOI: https://dx.doi.org/10.1016/j.oceaneng.2022.112595]
200. De Luca, R.; Ferraro, A.; Galli, A.; Gallo, M.; Moscato, V.; Sperli, G. A deep attention based approach for predictive maintenance applications in IoT scenarios. J. Manuf. Technol. Manag.; 2023; 34, pp. 535-556. [DOI: https://dx.doi.org/10.1108/JMTM-02-2022-0093]
201. Barraza, J.F.; Droguett, E.L.; Martins, M.R. FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis. Expert Syst. Appl.; 2024; 237, 121670. [DOI: https://dx.doi.org/10.1016/j.eswa.2023.121670]
202. Χριστοδούλου, Γ.A. Interpretable Predictive Maintenance: Global and Local Dimensionality Reduction Approach. Ph.D. Thesis; Aristotle University of Thessaloniki: Thessaloniki, Greece, 2022.
203. Alfeo, A.L.; Cimino, M.G.; Vaglini, G. Degradation stage classification via interpretable feature learning. J. Manuf. Syst.; 2022; 62, pp. 972-983. [DOI: https://dx.doi.org/10.1016/j.jmsy.2021.05.003]
204. Giordano, D.; Giobergia, F.; Pastor, E.; La Macchia, A.; Cerquitelli, T.; Baralis, E.; Mellia, M.; Tricarico, D. Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case. Comput. Ind.; 2022; 134, 103554. [DOI: https://dx.doi.org/10.1016/j.compind.2021.103554]
205. Jing, T.; Zheng, P.; Xia, L.; Liu, T. Transformer-based hierarchical latent space VAE for interpretable remaining useful life prediction. Adv. Eng. Inform.; 2022; 54, 101781. [DOI: https://dx.doi.org/10.1016/j.aei.2022.101781]
206. Mazzocca, C.; Romandini, N.; Mendula, M.; Montanari, R.; Bellavista, P. TruFLaaS: Trustworthy Federated Learning as a Service. IEEE Internet Things J.; 2023; 10, pp. 21266-21281. [DOI: https://dx.doi.org/10.1109/JIOT.2023.3282899]
207. Ghasemkhani, B.; Aktas, O.; Birant, D. Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing. Machines; 2023; 11, 322. [DOI: https://dx.doi.org/10.3390/machines11030322]
208. Abbas, A.N.; Chasparis, G.; Kelleher, J.D. Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines. arXiv; 2022; arXiv: 2206.13433
209. Gilles, O.; Pérez, D.G.; Brameret, P.-A.; Lacroix, V. Securing IIot communications using OPC UA pubsub and trusted platform modules. J. Syst. Archit.; 2023; 134, 102797. [DOI: https://dx.doi.org/10.1016/j.sysarc.2022.102797]
210. Unal, P.; Albayrak, Ö.; Jomâa, M.; Berre, A.J. Data-driven artificial intelligence and predictive analytics for the maintenance of industrial machinery with hybrid and cognitive digital twins. Technologies and Applications for Big Data Value; Springer: New York, NY, USA, 2022; pp. 299-319.
211. Barig, B.; Balzereit, K.; Hutschenreuther, T. Applying OPC-UA for factory-wide industrial assistance systems. Proceedings of the 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS); Sundsvall, Sweden, 27–29 May 2019; pp. 1-4.
212. Kedari, S.; Kulkarni, S.; Vishwakarma, C.; Korgaonkar, J.; Warke, N. Remote monitoring and predictive maintenance in bottle filling process using industry 4.0 concepts. AIP Conference Proceedings; AIP Publishing: New York, NY, USA, 2022.
213. Dosluoglu, T.; MacDonald, M. Circuit Design for Predictive Maintenance. arXiv; 2022; arXiv: 2211.10248[DOI: https://dx.doi.org/10.54364/AAIML.2022.1136]
214. Balla, M.; Haffner, O.; Kučera, E.; Cigánek, J. Educational Case Studies: Creating a Digital Twin of the Production Line in TIA Portal, Unity, and Game4Automation Framework. Sensors; 2023; 23, 4977. [DOI: https://dx.doi.org/10.3390/s23104977] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37430895]
215. Ryalat, M.; ElMoaqet, H.; AlFaouri, M. Design of a smart factory based on cyber-physical systems and Internet of Things towards Industry 4.0. Appl. Sci.; 2023; 13, 2156. [DOI: https://dx.doi.org/10.3390/app13042156]
216. Killeen, P. Knowledge-Based Predictive Maintenance for Fleet Management. Ph.D. Thesis; Université d’Ottawa/University of Ottawa: Ottawa, ON, Canada, 2020.
217. Shin, S.-J. An opc ua-compliant interface of data analytics models for interoperable manufacturing intelligence. IEEE Trans. Ind. Inform.; 2020; 17, pp. 3588-3598. [DOI: https://dx.doi.org/10.1109/TII.2020.3024628]
218. Bojarczuk, G.; Mazur, M.; Wojciechowski, A.; Olszewski, M. Artificial Intelligence in Predicting Abnormal States in a Robotic Production Stand. Pomiary Autom. Robot.; 2021; 25, pp. 5-22. [DOI: https://dx.doi.org/10.14313/PAR_241/5]
219. Allahloh, A.S.; Sarfraz, M.; Ghaleb, A.M.; Al-Shamma’a, A.A.; Hussein Farh, H.M.; Al-Shaalan, A.M. Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance. Sustainability; 2023; 15, 8808. [DOI: https://dx.doi.org/10.3390/su15118808]
220. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance. Procedia Comput. Sci.; 2022; 200, pp. 611-619. [DOI: https://dx.doi.org/10.1016/j.procs.2022.01.259]
221. Hafeez, T.; Xu, L.; Mcardle, G. Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access; 2021; 9, pp. 49355-49371. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3069137]
222. Foukalas, F. Cognitive IoT platform for fog computing industrial applications. Comput. Electr. Eng.; 2020; 87, 106770. [DOI: https://dx.doi.org/10.1016/j.compeleceng.2020.106770]
223. Fernandes, M.; Canito, A.; Mota, D.; Corchado, J.M.; Marreiros, G. Service-oriented architecture for data-driven fault detection. Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence; Salamanca, Spain, 14 May 2021; pp. 179-189.
224. Bulut, B.; Ketmen, H.B.; Atalay, A.S.; Herkiloğlu, O.; Salokangas, R. An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. Proceedings of the 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA); Kocaeli, Turkey, 25–27 August 2021; pp. 1-5.
225. Greci, L. XR for Industrial Training & Maintenance. Roadmapping Extended Reality: Fundamentals and Applications; John Wiley & Sons: New York, NY, USA, 2022; pp. 309-320.
226. Pujana, A.; Esteras, M.; Perea, E.; Maqueda, E.; Calvez, P. Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation. Energies; 2023; 16, 861. [DOI: https://dx.doi.org/10.3390/en16020861]
227. Stamoulis, K. Innovations in the Aviation MRO: Adaptive, Digital, and Sustainable Tools for Smarter Engineering and Maintenance; Eburon Academic Publishers: Delft, The Netherlands, 2022.
228. Rossini, R.; Prato, G.; Conzon, D.; Pastrone, C.; Pereira, E.; Reis, J.; Gonçalves, G.; Henriques, D.; Santiago, A.R.; Ferreira, A. AI environment for predictive maintenance in a manufacturing scenario. Proceedings of the 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA); Vasteras, Sweden, 7–10 September 2021; pp. 1-8.
229. Klein, P.; Bergmann, R. Generation of Complex Data for AI-based Predictive Maintenance Research with a Physical Factory Model. Proceedings of the ICINCO (1); Prague, Czech Republic, 29–31 July 2019; pp. 40-50.
230. Wolfartsberger, J.; Zenisek, J.; Wild, N. Data-driven maintenance: Combining predictive maintenance and mixed reality-supported remote assistance. Procedia Manuf.; 2020; 45, pp. 307-312. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.04.022]
231. Bekar, E.T.; Nyqvist, P.; Skoogh, A. An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Adv. Mech. Eng.; 2020; 12, 1687814020919207. [DOI: https://dx.doi.org/10.1177/1687814020919207]
232. Kamariotis, A.; Tatsis, K.; Chatzi, E.; Goebel, K.; Straub, D. A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance. Reliab. Eng. Syst. Saf.; 2024; 242, 109723. [DOI: https://dx.doi.org/10.1016/j.ress.2023.109723]
233. Kong, Q.; Lu, R.; Yin, F.; Cui, S. Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud. IEEE Trans. Intell. Transp. Syst.; 2020; 22, pp. 5060-5070. [DOI: https://dx.doi.org/10.1109/TITS.2020.3011931]
234. Liu, Y.; Yu, W.; Dillon, T.; Rahayu, W.; Li, M. Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring. IEEE Trans. Ind. Inform.; 2021; 18, pp. 1345-1354. [DOI: https://dx.doi.org/10.1109/TII.2021.3091774]
235. von Enzberg, S.; Naskos, A.; Metaxa, I.; Köchling, D.; Kühn, A. Implementation and transfer of predictive analytics for smart maintenance: A case study. Front. Comput. Sci.; 2020; 2, 578469. [DOI: https://dx.doi.org/10.3389/fcomp.2020.578469]
236. Bibri, S.E.; Jagatheesaperumal, S.K. Harnessing the potential of the metaverse and artificial intelligence for the internet of city things: Cost-effective XReality and synergistic AIoT technologies. Smart Cities; 2023; 6, pp. 2397-2429. [DOI: https://dx.doi.org/10.3390/smartcities6050109]
237. Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst.; 2022; 35, pp. 27730-27744.
238. Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y.T.; Li, Y.; Lundberg, S. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv; 2023; arXiv: 2303.12712
239. Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H.W.; Sutton, C.; Gehrmann, S. Palm: Scaling language modeling with pathways. arXiv; 2022; arXiv: 2204.02311
240. Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F. Llama: Open and efficient foundation language models. arXiv; 2023; arXiv: 2302.13971
241. Rane, N. Potential Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Architectural Engineering. 2023; Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4607767 (accessed on 19 January 2024).
242. Rane, N. Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry. 2023; Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598258 (accessed on 19 January 2024).
243. Ghimire, P.; Kim, K.; Acharya, M. Generative ai in the construction industry: Opportunities & challenges. arXiv; 2023; arXiv: 2310.04427
244. Rane, N.L. Multidisciplinary Collaboration: Key Players in Successful Implementation of ChatGPT and Similar Generative Artificial Intelligence in Manufacturing, Finance, Retail, Transportation, and Construction Industry. 2023; Available online: https://osf.io/preprints/osf/npm3d (accessed on 19 January 2024).
245. Voß, S. Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?. Sustainability; 2023; 15, 9625. [DOI: https://dx.doi.org/10.3390/su15129625]
246. Akhbardeh, F.; Desell, T.; Zampieri, M. NLP tools for predictive maintenance records in MaintNet. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations; Suzhou, China, 4–7 December 2020; pp. 26-32.
247. Singh, J.; Samborowski, L.; Mentzer, K. A Human Collaboration with ChatGPT: Developing Case Studies with Generative AI. Proceedings of the ISCAP Conference on Information Systems and Computing Education; Albuquerque, NM, USA, 1–4 November 2023; ISSN 2473–4901 pp. 1-11.
248. Song, L.; Zhang, C.; Zhao, L.; Bian, J. Pre-Trained Large Language Models for Industrial Control. arXiv; 2023; arXiv: 2308.03028
249. Frederico, G.F. ChatGPT in Supply Chains: Initial Evidence of Applications and Potential Research Agenda. Logistics; 2023; 7, 26. [DOI: https://dx.doi.org/10.3390/logistics7020026]
250. Wang, J.; Zhang, L.; Yang, Y.; Zhuang, Z.; Qi, Q.; Sun, H.; Lu, L.; Feng, J.; Liao, J. Network Meets ChatGPT: Intent Autonomous Management, Control and Operation. J. Commun. Inf. Netw.; 2023; 8, pp. 239-255. [DOI: https://dx.doi.org/10.23919/JCIN.2023.10272352]
251. Bodenhausen, U.; Braatz, A. Next Level AI-Based Development: From Understanding to Mastering of the Key Elements. Proceedings of the International Stuttgart Symposium; Stuttgart, Germany, 26–27 September 2023; pp. 549-560.
252. Stewart, M.; Hodkiewicz, M.; Li, S. Large Language Models for Failure Mode Classification: An Investigation. arXiv; 2023; arXiv: 2309.08181
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Karakose, Mehmet 2
; Kırımça, Necim 3 1 Mechatronics Engineering Department, Engineering Faculty, Fırat University, Elazig 23119, Turkey
2 Computer Engineering Department, Engineering Faculty, Fırat University, Elazig 23119, Turkey;
3 R&D Department, Albayrak Makine Elektronik A.S., Eskisehir 26110, Turkey;