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Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System

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Abstract

Bearing incipient fault characteristics are always submerged in strong background noise with weak fault characteristics, so that the incipient fault is hard to detect. Stochastic resonance (SR) is accepted to be an effective way to detect the incipient; however, output saturation may occur if bistable SR is adopted. In this paper, a bearing incipient fault detection method is proposed based on triple-well potential system and SR mechanism. The achievement of SR highly replays on the nonlinear system which is adopted a triple-well potential function in this paper. Therefore, the parameters in the nonlinear system are optimized by particle swarm optimization algorithm, and the objective of optimization is to maximize the signal-to-noise ratio of the fault signal. After optimization, the optimal system parameters are obtained thereby the resonance effect is generated and the bearing incipient fault characteristic is enhanced. The proposed method is validated by simulation verification and engineering application. The results show that the method is effective to detect an incipient signal from heavy background noise and can obtain better outputs compared with bistable SR.

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References

  1. XIA J Z, LIU Y H, LENG Y G, et al. Analysis of methods of weak signal detection [J]. Noise and Vibration Control, 2011, 31(3): 156–161 (in Chinese).

    Google Scholar 

  2. LÜ Y, LI Y R, WANG Z G, et al. Research on a extraction method for weak fault signal and its application [J]. Journal of Vibration Engineering, 2007, 20(1): 24–28 (in Chinese).

    Google Scholar 

  3. YE Q H, HUANG H N, ZHANG C H. Design of stochastic resonance systems in weak signal detection [J]. Acta Electronica Sinica, 2009, 37(1): 216–220 (in Chinese).

    Google Scholar 

  4. LIN J, QU L S. Feature detection and fault diagnosis based on continuous wavelet transform [J]. Chinese Journal of Mechanical Engineering, 2000, 36(12): 95–100 (in Chinese).

    Article  Google Scholar 

  5. WANG T Y, HE H L, WANG G F, et al. Rolling-bearings fault diagnosis based-on empirical mode decomposition and least square support vector machine [J]. Chinese Journal of Mechanical Engineering, 2007, 43(4): 88–92 (in Chinese).

    Article  Google Scholar 

  6. LI C Y, XU M Q, GUO S. Fault diagnosis of rolling element bearing based on principal component analysis of acoustic signal [J]. Technical Acoustics, 2008, 27(2): 271–274 (in Chinese).

    Google Scholar 

  7. CHENG J S, ZHANG K, YANG Y. Local mean decomposition method and its application to roller bearing fault diagnosis [J]. China Mechanical Engineering, 2009, 20(22): 2711–2717 (in Chinese).

    Google Scholar 

  8. BENZI R, SUTERA A, VULPIANI A. The mechanism of stochastic resonance [J]. Journal of Physics A: Mathematical and General, 1981, 14(11): L453–L457.

    Article  MathSciNet  Google Scholar 

  9. HU N Q, CHEN M, WEN X S. Application of stochastic resonance theory for early detecting rub-impact fault of rotor system [J]. Chinese Journal of Mechanical Engineering, 2001, 37(9): 88–91 (in Chinese).

    Article  Google Scholar 

  10. LENG Y G, WANG T Y. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise [J]. Acta Physica Sinica, 2003, 52(10): 2432–2437 (in Chinese).

    Article  Google Scholar 

  11. CHEN M, HU N Q, QIN G J, et al. Application of parameter-tuning stochastic resonance for detecting early mechanical faults [J]. Journal of Mechanical Engineering, 2009, 45(4): 131–135 (in Chinese).

    Article  Google Scholar 

  12. TAN J Y, CHEN X F, LEI Y G, et al. Adaptive frequency-shifted and re-scaling stochastic resonance with applications to fault diagnosis [J]. Journal of Xi’an Jiaotong University, 2009, 43(7): 69–73 (in Chinese).

    Google Scholar 

  13. LEI Y G, HAN D, LIN J, et al. New adaptive stochastic resonance method and its application to fault diagnosis [J]. Journal of Mechanical Engineering, 2012, 48(7): 62–67 (in Chinese).

    Article  Google Scholar 

  14. ZHOU Y F, WANG H J, ZUO Y B, et al. Bearing fault diagnosis based on scale transformation stochastic resonance [J]. Journal of Beijing Information Science & Technology University, 2015, 30(6): 68–72 (in Chinese).

    Google Scholar 

  15. QIAO Z J, LEI Y G, LIN J, et al. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 84: 731–746.

    Article  Google Scholar 

  16. KENNEDY J, EBERHART R. Particle swarm optimization [C]//International Conference on Neural Networks. Perth, Australia: IEEE, 1995, 1942–1948.

    Google Scholar 

  17. LI J M, CHEN X F, HE Z J. Adaptive monostable stochastic resonance based on PSO with application in impact signal detection [J]. Journal of Mechanical Engineering, 2011, 47(21): 58–63 (in Chinese).

    Article  Google Scholar 

  18. LI Y B, ZHANG B L, LIU Z X, et al. Adaptive stochastic resonance method based on quantum particle swarm optimization [J]. Acta Physica Sinica, 2014, 63(16): 40–47 (in Chinese).

    Google Scholar 

  19. WANG Z X, GUO L. Research on weak signal detection method based based on adaptive stochastic resonance [J]. Computer Measurement & Control, 2018, 26(1): 42–46 (in Chinese).

    Google Scholar 

  20. XIAO L, XIA T B, PAN E S, et al. A novel weak bearing fault detection method based on vibrational resonance [C]//2018 Prognostics and System Health Management Conference (PHM-Chongqing). Chongqing, China: IEEE, 2018, 100–104.

    Chapter  Google Scholar 

  21. BECHHOEFER E. Condition based maintenance fault database for testing of diagnostic and prognostics algorithms [EB/OL]. [2019-08-28]. https://www.mfpt.org/fault-data-sets/.

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Authors and Affiliations

  1. College of Mechanical Engineering, Donghua University, Donghua, Shanghai, 201620, China

    Ziwen Liu  (刘子文), Lei Xiao  (肖雷) & Jinsong Bao  (鲍劲松)

  2. Mormount (Shanghai) Engineering Co., Ltd., Shanghai, 203201, China

    Qingbao Tao  (陶清宝)

Authors
  1. Ziwen Liu  (刘子文)
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  2. Lei Xiao  (肖雷)
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  3. Jinsong Bao  (鲍劲松)
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  4. Qingbao Tao  (陶清宝)
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Corresponding author

Correspondence to Lei Xiao  (肖雷).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 51705321), the Fundamental Research Funds for the Central Universities (Nos. 2232019D3-29 and 2232017A-03), the China Postdoctoral Science Foundation (No. 2017M611576), the Shanghai Industrial Internet Innovation and Development Project (No. 2018-GYHLW-01003), and the Energy Intelligent Management Application Platform Project Based on Artificial Intelligence (No. 2018-RGZN-02055)

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Liu, Z., Xiao, L., Bao, J. et al. Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System. J. Shanghai Jiaotong Univ. (Sci.) 26, 482–487 (2021). https://doi.org/10.1007/s12204-020-2238-4

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  • DOI: https://doi.org/10.1007/s12204-020-2238-4

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