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Introduction
Predictive maintenance requires regular monitoring of the state of bearings, which are crucial parts of spinning machinery [1]. Bearing vibration signals are frequently employed for failure detection, although noise greatly affects these signals [2]. Research indicates that the integration of noise-resistant techniques, like the use of a Gaussian noise layer, enhances the efficacy of machine learning models in the diagnosis of bearing defects [3]. Specifically, in real-world contexts, ensemble approaches and deep learning models trained with noisy data have shown better accuracy and robustness. Predictive maintenance models have a major problem with noise in sensor data [4]. Although machine learning techniques and conventional signal processing techniques [5] have been used to reduce the impact of noise, these approaches have drawbacks. Developing models that are resistant to noise can be facilitated by the utilization of Gaussian noise layers, which is one of the latest developments in deep learning. The other way of improving the durability of predictive models is to use ensemble approaches [6]. To further increase the dependability of predictive maintenance systems, future research should concentrate on tackling the problems of noise variability and model interpretability. The goal of predictive maintenance is to anticipate and stop equipment problems by utilizing data from machinery [7]. The difficulty in this problem stems from the noise in the sensor data, which can have a big impact on how well machine learning models work. Numerous things, including inaccurate sensors, external disturbances, and interference from electrical components, might introduce noise. Thus, creating models that are resistant to noise is essential for accurate predictive maintenance [8]. Due to the inherent noise in sensor data, false alarms or the failure to notice possible breakdowns may occur from faulty predictions. Noise decreases the signal quality and interferes with the feature extraction process, making the input to machine learning models unreliable. This issue is especially common in industries where sensor data is gathered in challenging and variable environments. In these kinds of situations, vibration, temperature changes, electromagnetic interference, and mechanical wear and tear on sensors can all produce noise [9]. Many methods have been used to lower noise in sensor data. Conventional methods involve signal processing techniques including Fourier transforms, wavelet transforms, and filtering [10]. To eliminate high-frequency noise components, filtering techniques such...