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Research Papers

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

[+] Author and Article Information
Bingbing Hu

Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: hubb416@xaut.edu.cn

Chang Guo

Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: 2160820043@stu.xaut.edu.cn

Jimei Wu

Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: wujimei@xaut.edu.cn

Jiahui Tang

Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: 2170820024@stu.xaut.edu.cn

Jialing Zhang

School of Mechanical and Precision Instrument Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: 1180210017@stu.xaut.edu.cn

Yuan Wang

Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an University of Technology,
Xi’an 710048, China
e-mail: 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 May 10, 2019. Assoc. Editor: Huageng Luo.

J. Vib. Acoust 141(4), 041016 (May 10, 2019) (9 pages) Paper No: VIB-18-1529; doi: 10.1115/1.4043063 History: Received December 02, 2018; Accepted February 28, 2019

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 demonstrates that the proposed method not only obtains a larger output SNR but also requires less time for the optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

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Figures

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Fig. 1

(a) Output saturation phenomenon for the classic SR and (b) potential function for the PSR

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Fig. 2

The flow for the proposed APSR method

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Fig. 3

The time-domain waveform and the zoom-in power spectrum: (a) original simulation signal, (b) proposed method where a = 2.21 and b = 0.37, (c) APSR method based on the AFS algorithm where a = 1.85 and b = 0.45, and (d) classic SR based on the GWO algorithm where a = 2.00 and b = 0.03

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Fig. 4

The comparison results of diagnosis for bearing fault on inner race: (a) the time-domain waveform and the zoom-in power spectrum, (b) the envelope signal and the zoom-in power spectrum, (c) the time-domain waveform and the zoom-in power spectrum for the proposed method where a = 0.87 and b = 0.12, and (d) the time-domain waveform and the power spectrum for the APSR method based on the AFS algorithm where a = 1.25 and b = 0.25

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Fig. 5

The comparison results of diagnosis for bearing fault on outer race: (a) the time-domain waveform and the zoom-in power spectrum, (b) the envelope signal and the zoom-in power spectrum, (c) the time-domain waveform and the zoom-in power spectrum for the proposed method where a = 1.65 and b = 0.18, and (d) the time-domain waveform and the power spectrum for the APSR method based on the AFS algorithm where a = 1.97 and b = 0.27

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Fig. 6

The comparison results of diagnosis for bearing fault on the rolling element: (a) the time-domain waveform and the zoom-in power spectrum, (b) the envelope signal and the zoom-in power spectrum, (c) the time-domain waveform and the zoom-in power spectrum of the proposed method where a = 0.91 and b = 0.15, and (d) the time-domain waveform and the power spectrum for the APSR method based on the AFS algorithm where a = 2.39 and b = 0.20

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Fig. 7

Printing machine ink roller bearing test rig

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Fig. 8

Inner ring defect for rolling bearing

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Fig. 9

The comparison results of diagnosis for bearing fault on the rolling element: (a) the time-domain waveform and the power spectrum, (b) the envelope signal and the power spectrum, (c) the time-domain waveform and the power spectrum for the proposed method where a = 0.16 and b = 0.57, and (d) the time-domain waveform and the power spectrum for the APSR method based on the AFS algorithm where a = 0.85 and b = 0.31

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