Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network

This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to...

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Bibliographic Details
Published in:Processes 2020-10, Vol.8 (10), p.1322
Main Authors: Lee, Chun-Yao, Cheng, Yi-Hsin
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Failure
Fault detection
Feature extraction
Fourier transforms
Neural networks
Noise
Noise levels
Optimization techniques
Particle swarm optimization
Short circuits
Shutdowns
Signal analysis
Wavelet transforms
White noise
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Summary:This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr8101322