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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August 2019
Research-Article
An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis
Bingbing Hu,
Bingbing Hu
1
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: hubb416@xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: hubb416@xaut.edu.cn
1Corresponding author.
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Chang Guo,
Chang Guo
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: 2160820043@stu.xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: 2160820043@stu.xaut.edu.cn
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Jimei Wu,
Jimei Wu
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: wujimei@xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: wujimei@xaut.edu.cn
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Jiahui Tang,
Jiahui Tang
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: 2170820024@stu.xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: 2170820024@stu.xaut.edu.cn
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Jialing Zhang,
Jialing Zhang
School of Mechanical and Precision Instrument Engineering,
Xi’an 710048,
e-mail: 1180210017@stu.xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: 1180210017@stu.xaut.edu.cn
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Yuan Wang
Yuan Wang
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: 2160821068@stu.xaut.edu.cn
Xi’an University of Technology
,Xi’an 710048,
China
e-mail: 2160821068@stu.xaut.edu.cn
Search for other works by this author on:
Bingbing Hu
Faculty of Printing, Packaging and Digital Media Engineering,
Xi’an 710048,
e-mail: hubb416@xaut.edu.cn
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 710048,
e-mail: 2160820043@stu.xaut.edu.cn
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 710048,
e-mail: wujimei@xaut.edu.cn
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 710048,
e-mail: 2170820024@stu.xaut.edu.cn
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 710048,
e-mail: 1180210017@stu.xaut.edu.cn
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 710048,
e-mail: 2160821068@stu.xaut.edu.cn
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. Aug 2019, 141(4): 041016 (9 pages)
Published Online: May 10, 2019
Article history
Received:
December 2, 2018
Revision Received:
February 28, 2019
Accepted:
February 28, 2019
Citation
Hu, B., Guo, C., Wu, J., Tang, J., Zhang, J., and Wang, Y. (May 10, 2019). "An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis." ASME. J. Vib. Acoust. August 2019; 141(4): 041016. https://doi.org/10.1115/1.4043063
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