## 2 Related Work
### Vibration Defects in Rotating Machinery
The SWT ensures shift invariance in the signal, allowing it to better capture specific defect features [23]. Traditional vibration signal analysis has predominantly relied on frequency-based methods such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT). However, these approaches often struggle to effectively capture the complex and nonlinear characteristics of real-world machine faults. To address these limitations, recent studies have highlighted entropy-based techniques as promising alternatives for enhancing fault detection performance by quantifying the irregularity and disorder in signals [24]. * Phase Entropy (PE): This method measures the complexity of signals in phase space, allowing for the detection of subtle variations in nonlinear and chaotic systems [25]. * Multi-scale Entropy (MSE): By analyzing signals across multiple time scales and frequency bands, this approach improves the accuracy of fault detection [26]. * Image Quadrant Entropy (IQE): This technique converts vibration signals into image representations and calculates entropy in different quadrants, enhancing anomaly detection accuracy [27]. These entropy-based approaches provide a robust framework for analyzing complex vibration signals and have demonstrated significant potential in improving fault diagnosis across various engineering applications. While entropy-based techniques are effective for analyzing signal complexity, they rely on hand-crafted feature extraction, which may limit their generalization ability when applied to complex real-world datasets. In contrast, deep learning-based approaches offer a more powerful alternative by automatically learning hierarchical feature representations from raw vibration signals. Additionally, various signal processing techniques, such as high-pass and low-pass filtering, the Hilbert transform, and empirical mode decomposition (EMD), are widely used in vibration data analysis. These methods help extract key signal features, facilitating more accurate defect detection and localization. Traditionally, facility management has consisted primarily of regular preventive inspections and reactive maintenance after a failure has occurred. Preventive maintenance involves inspecting equipment at regular intervals and replacing parts when necessary, an approach that attempts to address problems before they occur. However, this approach can be time-consuming and costly, with the potential for unnecessary maintenance work even when the equipment has not actually failed. Reactive maintenance is an approach for fixing problems after a failure has occurred that can lead to downtime and costly losses. Predictive maintenance is a modern way of predicting and preventing failures in advance based on facility data. Predictive maintenance utilizes IoT sensors and data collection technologies to monitor the health of equipment in real time and assess the likelihood of failure through machine learning and deep learning algorithms [28]. Various algorithms for Remaining Useful Life (RUL) prediction, anomaly detection, and failure diagnosis are being developed to improve equipment utilization and reduce maintenance costs.