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research-article

An adaptive periodical stochastic resonance method based on grey wolf optimizer algorithm and its application in rolling bearing fault diagnosis

[+] Author and Article Information
Bingbing Hu

No. 5 Jinhua South Road, Beilin District, Xi'an Xi'an, 710048 China hubb416@xaut.edu.cn

Chang Guo

No. 5 Jinhua South Road, Beilin District, Xi'an Xi'an, Shaanxi 710048 China 2160820043@stu.xaut.edu.cn

Jimei Wu

NO.5 Jinhua South Road, Xi,an City,China Xi'an, 710048 China wujimei@xaut.edu.cn

Jiahui Tang

No. 5 Jinhua South Road, Beilin District, Xi'an Xi'an, 710048 China 2170820024@stu.xaut.edu.cn

Jialing Zhang

No. 5 Jinhua South Road, Beilin District, Xi'an Xi'an, 710048 China 1180210017@stu.xaut.edu.cn

Yuan Wang

No. 5 Jinhua South Road, Beilin District, Xi'an Xi'an, 710048 China 2160821068@stu.xaut.edu.cn

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the Journal of Vibration and Acoustics. Manuscript received December 2, 2018; final manuscript received February 28, 2019; published online xx xx, xxxx. Assoc. Editor: Huageng Luo.

ASME doi:10.1115/1.4043063 History: Received December 02, 2018; Accepted February 28, 2019

Abstract

As a weak signal processing method that utilizes noise enhanced fault signals, stochastic resonance (SR) is widely used in mechanical fault diagnosis. However, the classic bistable SR has a problem with output saturation, which affects its ability to enhance fault characteristics. Moreover, it is difficult to implement SR when the fault frequency is not clear, which limits its application in engineering practice. To solve these problems, this paper proposed an adaptive periodical stochastic resonance (APSR) method based on the grey wolf optimizer (GWO) algorithm for rolling bearing fault diagnosis. The periodical stochastic resonance (PSR) model can independently adjust the system parameters and effectively avoid output saturation. The GWO algorithm is introduced to optimize the PSR model parameters to achieve adaptive detection of the input signal, and the output signal-to-noise ratio (SNR) is used as the objective function of the GWO algorithm. Simulated signals verify the validity of the proposed method. Furthermore, this method is applied to bearing fault diagnosis, experimental analysis demonstrate that the proposed method not only obtains a larger output SNR but also requires less time for optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

Copyright © 2019 by ASME
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