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Open AccessArticle

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

Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
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Symmetry 2019, 11(10), 1212; https://doi.org/10.3390/sym11101212
Received: 5 August 2019 / Revised: 25 September 2019 / Accepted: 27 September 2019 / Published: 29 September 2019
(This article belongs to the Special Issue Selected Papers from IIKII 2019 conferences in Symmetry)
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. View Full-Text
Keywords: empirical mode decomposition; pattern recognition; wavelet; empirical wavelet transform; convolutional neural network; induction motor; fourier transform; fault diagnosis empirical mode decomposition; pattern recognition; wavelet; empirical wavelet transform; convolutional neural network; induction motor; fourier transform; fault diagnosis
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Hsueh, Y.-M.; Ittangihal, V.R.; Wu, W.-B.; Chang, H.-C.; Kuo, C.-C. Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. Symmetry 2019, 11, 1212.

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