# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
###### Abstract  
Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet-CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could.  
wavelet; deep learning; fault diagnosis; induction motors; motor current signature analysis; convolutional neural networks +
Footnote †: journal: 8  
sufficient. Frequency domain analysis is an alternative with respect to frequency rather than time, and Fourier techniques are employed for this kind of analysis. Wavelet analysis is a combination of both frequency- and time-domain analyses, and it is ideal for nonstationary signals composed of dynamic frequencies, according to [5]. Wavelet analysis is useful for fault diagnostic implementations when fast Fourier transform (FFT) is not efficient or time information about the fault is needed [5].  
Machine learning (ML) is a subfield of artificial intelligence that regards techniques focusing on automatically recognizing patterns in data and drawing inferences. Deep learning (DL) is a subdomain of ML, and its algorithms imitate the way in which the human brain gains knowledge. DL and artificial neural networks (ANNs) are employed in various applications because of their high accuracy and generalization power, as highlighted in our previous studies [6; 7; 8].  
This paper proposes a fault diagnostic system based on a hybrid discrete wavelet-CNN method. A 3-phase motor was simulated in MATLAB Simulink under healthy and faulty stator winding operation. Preliminary condition monitoring was achieved by performing discrete wavelet transform (DWT) analysis of the 3-phase stator currents. The next step involves using the results as input in a CNN model to automatically classify faults.  
The remainder of this paper is organized as follows: Section 2 presents a review of related works. Section 3 describes the simulation model of the motor. Section 4 discusses the diagnostic methodology. The results of descriptive analysis, and the hybrid model's parameters and accuracy are discussed in Section 5. Section 6 draws the conclusions and outlines future directions.