Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

Abstract:

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

• Periodical: Advanced Materials Research (Volume 382)
• Pages: 163-166
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.382.163
• Online since: November 2011
• Authors: Qing Xin Zhang, Jin Li, Hai Bin Li, Chong Liu
• Keywords: BP Neural Network (BPNN), Fault Diagnosis, Park Vector, Wavelet Packet Decomposition

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