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research-article

Gearbox Fault Diagnosis Based on Selective Integration Feature-based Soft Competition ART

[+] Author and Article Information
Xiao-Jin Wan

School of Automotive Engineering, Hubei Key Laboratory of Advanced Technology for Automotive Components; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
wxj_2001@163.com

Licheng Liu

School of Automotive Engineering, Hubei Key Laboratory of Advanced Technology for Automotive Components; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
licheng_liu@163.com

Zengbing Xu

College of machinery and automation, Wuhan University of Science and Technology, Wuhan 430070, China
xuzengbing@163.com

Zhigang Xu

School of Automotive Engineering, Hubei Key Laboratory of Advanced Technology for Automotive Components; Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
zhigangxu@whut.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4041776 History: Received June 20, 2018; Revised October 11, 2018

Abstract

In this work, a Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) diagnosis model based on multi-feature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional Fuzzy ART(FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the Fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with Fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.

Copyright (c) 2018 by ASME
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