## 2 Related Works  
In recent years, machine learning and signal processing techniques have made significant advances in fault detection and predictive maintenance in rotating machines. The increasing availability of real and synthetic datasets has offered researchers the opportunity to develop various algorithms in areas such as early fault diagnosis, system monitoring, and maintenance planning and test them on different datasets. This section focuses on current studies on motor and bearing fault detection using vibration analysis, signal processing methods, and machine learning-based approaches. The reviewed literature presents significant developments in the field by addressing the effectiveness and limitations of existing approaches within the framework of the methods used, the nature of the data, and the findings obtained. [PERSON] et al. [3] provided a comprehensive review of vibration monitoring techniques widely used for the predictive maintenance of rotating machinery. They emphasized the importance of vibration measurement as a non-invasive method to diagnose faults in rotating machine components, crucial in preventing unexpected maintenance stops and reducing overall operational costs in heavy industries. The conclusion was that selecting the appropriate vibration monitoring technique is crucial for effective assessments, and the integration of IoT devices could enable real-time monitoring to automatically diagnose mechanical faults. [PERSON] et al. [5] explored the use of Support Vector Machines (SVMs) to classify rotational unbalanced faults in rotary machines. They used vibration analysis data from an experimental setup using a rigid-shaft rotor. Their study demonstrated that the SVM is effective in classifying faults that are related to the balance of the machine's axis. It is hard to identify problems in rolling element bearings, especially when there are strong interfering signals from other machine components such as gears. [PERSON] and [PERSON] [37] explained how bearing signals have a cyclostationary behavior and used the spectral kurtosis technique to identify the frequency bands where signs of faults are most obvious. Since rolling element-bearing signals are stochastic, they can be separated from deterministic signals, such as those from gears. They coined the term pseudo-cyclostationary to model such signals and observed that discrete/random separation (DRS) is the most efficient technique for separation. DRS works by removing discrete frequency noise from signals. Later, spectral kurtosis is used to describe the identity of the frequency bands where the impulsivity is most pronounced. [PERSON] and [PERSON] [38] reviewed different signal processing techniques used to detect faults in rolling element bearings. They segmented these techniques into historical stages and highlighted recent advancements that enhanced the diagnostic capabilities. They categorized signal processing methods into three stages based on their historical development: Stage I (pre-2001), Stage II (2001-2010), and Stage III (2011-present). They conclude that selecting the correct signal processing technique is crucial for fault classification accuracy. [PERSON] [39] proposed a machine learning-based methodology to estimate the remaining service life of bearings. Feature extraction, data processing, dimension reduction, and merging steps were applied to calculate the machine's health indicator from vibration data. Vibration data collected in the time domain was converted to the frequency domain with the Welch method, and feature extraction was performed with traditional statistical indicators. [PERSON] et al. [9] proposed an improved bearing fault diagnosis method that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network by processing raw signal data without using any pre-processing or traditional feature extraction steps. While the CNN automatically extracted features from high-dimensional data, LSTM allowed for more effective identification of fault types by considering time dependencies.