Weak signal detection of composite multistable stochastic resonance with Woods–Saxon potential

Rui Gao; Shangbin Jiao; Yi Wang; Yujun Li

doi:10.1515/zna-2022-0120

Article

Weak signal detection of composite multistable stochastic resonance with Woods–Saxon potential

Rui Gao , Shangbin Jiao , Yi Wang and Yujun Li

Published/Copyright: August 9, 2022

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From the journal Zeitschrift für Naturforschung A Volume 77 Issue 10

Abstract

Weak signal detection under strong noise is a common problem in many engineering fields. The research on the detection theory and method of stochastic resonance (SR) has very important theoretical significance and application value for the realization of early weak fault diagnosis. In order to further enhance the weak signal processing capability of SR, an improved novel composite multistable potential well model is proposed by combining the tristable model and the Woods–Saxon model. The switching mechanism of the novel model constructed with the fusion of the tristable model and the Woods–Saxon model between different steady states is studied, the output response performance of SR system with the novel composite multistable model is analyzed. The adaptive synchronization optimization method of multiple system parameters adopts the differential brainstorming algorithm to realize the adaptive selection of multiple parameters. Simulation experiments are carried out on single and multiple low-frequency periodic signals and single and multiple high-frequency periodic signals under the Gaussian noise environment, simulation results indicate that the novel composite multistable SR system performs better. On the basis of this model, the composite multistable SR system is applied to the fault detection of rolling bearings, which has a good detection effect.

Keywords: bearing fault detection; composite multi-stable; stochastic resonance; weak signal detection

Corresponding author: Shangbin Jiao, Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China, E-mail: jiaoshangbin@xaut.edu.cn

Funding source: Open project of Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

Award Identifier / Grant number: 2020CP10

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 61871318

Funding source: Collaborative Innovation Center project of Shaanxi Provincial Department of Education

Award Identifier / Grant number: 20JY046

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This authors gratefully acknowledge the support of National Natural Science Foundation of China (No. 61871318), Open project of Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing (No. 2020CP10), Collaborative Innovation Center project of Shaanxi Provincial Department of Education (20JY046).
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] J. Z. Xia, H. Y. Liu, and Y. G. Leng, “Current situation analysis of weak signal detection methods,” Noise Vib. Control, vol. 31, p. 156, 2011.Search in Google Scholar

[2] L. L. Cao, J. Li, Z. Peng, Y. F. Zhang, W. D. Han, and H. G. Fu, “Rolling bearing fault diagnosis method based on improved wavelet threshold denoising,” Vib. Shock, vol. 41, p. 68, 2022.Search in Google Scholar

[3] W. B. Wang, X. D. Zhang, Y. C. Chang, et al.., “Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating,” Chin. Phys. B, vol. 25, p. 010202, 2016. https://doi.org/10.1088/1674-1056/25/1/010202.Search in Google Scholar

[4] L. Y. Li, H. Q. Wang, X. T. Xu, and X. Yang, “Application of chaotic oscillator in fault feature extraction of rolling bearing,” J. Dynam. Control, vol. 14, p. 177, 2016.Search in Google Scholar

[5] Y. G. Yao, “Time-varying coupling-induced logical stochastic resonance in a periodically driven coupled bistable system,” Chin. Phys. B, vol. 30, p. 060503, 2021. https://doi.org/10.1088/1674-1056/abd76c.Search in Google Scholar

[6] Y. F. Jin, “Stochastic resonance in an under-damped bistable system driven by harmonic mixing signal,” Chin. Phys. B, vol. 27, p. 050501, 2018. https://doi.org/10.1088/1674-1056/27/5/050501.Search in Google Scholar

[7] Z. J. Qiao, Y. G. Le, and J. Lin, “Stochastic resonance subject to multiplicative and additive noise: the influence of potential asymmetries,” Phys. Rev., vol. 94, p. 052214, 2016. https://doi.org/10.1103/physreve.94.052214.Search in Google Scholar PubMed

[8] D. X. Yang, F. S. Gu, G. J. Feng, Y. M. Yang, and B. Andrew, “Achieving high bit rate logical stochastic resonance in a bistable system by adjusting parameters,” Chin. Phys. B, vol. 24, p. 110502, 2015. https://doi.org/10.1088/1674-1056/24/11/110502.Search in Google Scholar

[9] J. M. Li, J. F. Zhang, and M. Li, “A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis,” Mech. Syst. Signal Process., vol. 114, p. 128, 2019. https://doi.org/10.1016/j.ymssp.2018.05.004.Search in Google Scholar

[10] T. T. Shi, X. M. XU, K. H. Sun, Y. P. Ding, and W. G. Huang, “Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback,” Chin. Phys. B, vol. 29, p. 050501, 2020. https://doi.org/10.1088/1674-1056/ab7e9f.Search in Google Scholar

[11] P. F. Xu, Y. F. Jin, and S. Xiao, “Stochastic resonance in a delayed triple-well potential driven by correlated noises,” Chaos, vol. 27, p. 13109, 2017. https://doi.org/10.1063/1.4994638.Search in Google Scholar PubMed

[12] S. B. Jiao, R. Yang, Q. Zhang, and G. Xie, “Asymmetric bistable stochastic resonance driven by stable noise,” Acta Phys. Sin., vol. 64, p. 020502, 2015.10.7498/aps.64.020502Search in Google Scholar

[13] J. M. Li, X. F. Chen, and Z. J. He, “Multi-stable stochastic resonance and its application research on mechanical fault diagnosis,” J. Sound Vib., vol. 332, p. 5999, 2013. https://doi.org/10.1016/j.jsv.2013.06.017.Search in Google Scholar

[14] Z. H. Lai and Y. G. Leng, “Dynamic response and stochastic resonance of tristable system,” J. Phys., vol. 64, p. 81, 2015. https://doi.org/10.7498/aps.64.200503.Search in Google Scholar

[15] Q. Q. Li, X. M. XU, L. Z. Yi, Y. P. Ding, J. F. Ding, and K. H. Sun, “Implication of two-coupled tri-stable stochastic resonance in weak signal detection,” Chin. Phys. B, vol. 27, p. 034203, 2018. https://doi.org/10.1088/1674-1056/27/3/034203.Search in Google Scholar

[16] L. F. He, Y. Y. Cui, T. Q. Zhang, G. Zhang, and Y. Song, “Analysis of weak signal detection based on tri-stable system under Levy noise,” Chin. Phys. B, vol. 25, p. 060501, 2016. https://doi.org/10.1088/1674-1056/25/6/060501.Search in Google Scholar

[17] H. B. Zhang, Q. B. He, and S. L. Lu, “Stochastic resonance with a joint Woods-Saxon and Gaussian potential for bearing fault diagnosis,” Math. Probl Eng., vol. 17, p. 315901, 2014.10.1155/2014/315901Search in Google Scholar

[18] S. L. Lu, Q. B. He, and F. R. Kong, “Stochastic resonance with Woods–Saxon potential for rolling element bearing fault diagnosis,” Mech. Syst. Signal Process., vol. 45, p. 488, 2014. https://doi.org/10.1016/j.ymssp.2013.12.004.Search in Google Scholar

[19] L. Lu, Y. Yuan, H. Wang, X. Zhao, and J. J. Zheng, “A new second-order tristable stochastic resonance method for fault diagnosis,” Symmetry, vol. 11, p. 080965, 2019. https://doi.org/10.3390/sym11080965.Search in Google Scholar

[20] Y. H. Zhou, X. M. Xu, L. Z. Yi, P. Ding, J. F. Ding, and K. H. Sun, “Novel Woods–Saxon stochastic resonance system for weak signal detection,” Chin. Phys. B, vol. 29, p. 040503, 2020. https://doi.org/10.1088/1674-1056/ab75ca.Search in Google Scholar

[21] G. Zhang, J. B. Shi, and T. Q. Zhang, “A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings,” J. Electron. Meas. Instrum., vol. 32, p. 142, 2018.10.1016/j.cjph.2017.11.013Search in Google Scholar

[22] Z. Y. Tao, C. H. Lu, Z. X. Zha, et al.., “Multi-frequency periodic weak signal detection based on single-well potential stochastic resonance,” J. Electron. Meas. Instrum., vol. 28, pp. 171–177, 2014. https://doi.org/10.1007/978-3-030-75793-9_51.Search in Google Scholar

[23] S. B. Jiao, X. X. Qiao, S. Lei, and W. Jiang, “A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential mode,” Chin. J. Phys., vol. 59, p. 138, 2019. https://doi.org/10.1016/j.cjph.2019.02.031.Search in Google Scholar

[24] S. B. Jiao, D. Sun, D. Liu, Y. L. Wu, and Q. Zhang, “Vibration resonance of a periodic potential system under stable noise,” Acta Phys. Sin., vol. 66, p. 100501, 2017.10.7498/aps.66.100501Search in Google Scholar

[25] Q. Ma, D. Huang, and J. Yang, “Adaptive stochastic resonance in second-order system with general scale transformation for weak feature extraction and its application in bearing fault diagnosis,” Fluc. Noise Lett., vol. 17, p. 1850009, 2018.10.1142/S0219477518500098Search in Google Scholar

[26] S. Lu, Q. He, and F. R. Kong, “Effects of underdamped step-varying second-order stochastic resonance for weak signal detection,” Digit. Signal Process., vol. 36, p. 93, 2015. https://doi.org/10.1016/j.dsp.2014.09.014.Search in Google Scholar

[27] G. Zhang, T. Yi, and T. Zhang, “A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings,” Acta Phys. Sin., vol. 56, p. 145, 2018.10.1016/j.cjph.2017.11.013Search in Google Scholar

Received: 2022-04-26

Revised: 2022-06-27

Accepted: 2022-06-30

Published Online: 2022-08-09

Published in Print: 2022-10-27

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Articles in the same Issue

https://doi.org/10.1515/zna-2022-0120

Keywords for this article

bearing fault detection; composite multi-stable; stochastic resonance; weak signal detection