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

doi:10.1007/s12204-020-2238-4

Abstract

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.

Key words: bearing| stochastic resonance (SR)| fault detection| triple-well potential system| particle swarm

摘要： 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.

关键词: bearing| stochastic resonance (SR)| fault detection| triple-well potential system| particle swarm

CLC Number:

TP 391.7

LIU Ziwen (刘子文), XIAO Lei (肖雷), BAO Jinsong (鲍劲松), TAO Qingbao (陶清宝) . Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 482-487.

References 21

[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).
[2]	L¨U 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).
[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).
[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).
[5]	WANG T Y, HE H L, WANG G F, et al. Rollingbearings 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).
[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).
[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).
[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.
[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).
[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).
[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).
[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).
[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).
[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).
[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.
[16]	KENNEDY J, EBERHART R. Particle swarm optimization [C]//International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[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).
[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).
[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).
[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.
[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/.