Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

• Published in: Symmetry (Basel) Vol. 11; no. 10; p. 1212
• Main Authors: Hsueh, Yu-Min, Ittangihal, Veeresh Ramesh, Wu, Wei-Bin, Chang, Hong-Chan, Kuo, Cheng-Chien
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2019
• Subjects: Artificial neural networks; convolutional neural network; Decomposition; Deep learning; empirical mode decomposition; empirical wavelet transform; Fault detection; Fault diagnosis; Feature extraction; Feature selection; fourier transform; Gray scale; induction motor; Induction motors; Machine learning; Machinery; Methodology; Neural networks; Pattern recognition; Signal processing; Vibration; wavelet; Wavelet transforms
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym11101212

Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.