# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 2 Related Works  
Recently, researchers have separately utilized DL and wavelets to diagnose various faults in power systems. [PERSON] et al. [9] presented a review of recent advances, mentioning applications of ANNs, fuzzy logic, and wavelet transform. [PERSON] et al. [10] studied the stator current time series from an experimental setup of an IM for both healthy and faulty operation. Statistical features were extracted from the acquisitioned raw time series and then fed into the ANN for classification. In this way, the authors claimed that the time required for training the DL model was reduced. Similarly, [PERSON] et al. [11] used a DL model for the interturn fault diagnosis of a transformer. The time series included both primary and secondary voltage and current waveforms. The signals were obtained from MATLAB Simulink simulations. In total, the study included 16 different interturn short-circuit faults, and the time series with the corresponding labels were fed into an autoencoder to classify the fault. [PERSON] et al. [5] simulated the stator winding interturn short circuits of a permanent-magnet synchronous motor. On the basis of stator current analysis, the authors concluded that time-frequency domain analysis with DWT is more reliable than frequency domain analysis with FFT. [PERSON] et al. [12] applied DWT to determine if the operation of a transformer was healthy or faulty. The transformer was simulated in MATLAB Simulink, and the authors highlighted that there was a change in the analyzed DWT wave shape when a fault occurred. Lastly, [PERSON] et al. [13] studied signal-processing techniques such as fast Fourier transform, short-time Fourier transform, and mainly continuous wavelet transform for the diagnosis of short circuits in the stator of a simulated 3-phase IM. Additionally, the authors applied many DL models, such as ANN, CNN, and RNN, to the raw time series of the currents, and successfully identified the type of fault. A new trend in fault diagnosis is to apply signal processing, such as the various wavelet techniques for feature extraction, and then to use the extracted data in DL methods. [PERSON] et al. [14] studied an experimental setup and proposed a diagnostic system in a 3-phase IM. Specifically, five different types of fault and a normal operation could be classified after current analysis. By applying an empirical wavelet transform, the raw signals were transformed into two-dimensional grayscale images retaining valuable information. Then, a CNN model was utilized to detect the faults by automatically extractingrobust features from grayscale images. Towards the same direction, [PERSON] et al. [15] studied an experimental rotor system, and the authors concluded that CNN models could reach higher classification accuracy when the models had been trained with scalograms of continuous wavelet transform (CWT) instead of hand-crafted features from the signals. [PERSON] et al. [16] presented a comparative study of ANN and support vector machines (SVMs) using CWT for the diagnosis and classification of rolling element bearing faults. SVM are considered supervised ML models and are implemented as advanced signal-processing techniques. The signals were obtained from experiments, and statistical features were extracted from the wavelet coefficients. The results showed that SVMs are more efficient than ANNs. [PERSON] et al. [17] presented a novel method, the deep order-wavelet convolutional variational autoencoder. Specifically, the authors presented a method on the experimental vibration data of a motor to diagnose bearing faults under fluctuating speed conditions. [PERSON] et al. [18] designed a similar method to ours for detecting stator interturn winding faults, but focusing on 2D data (images). DWT, as a time-frequency feature extraction method, was utilized before DL implementation. DL models included three advanced 2D CNN architectures (Inception, MobileNet, Xception).