J Shanghai Jiaotong Univ Sci ›› 2021, Vol. 26 ›› Issue (4): 482-487.doi: 10.1007/s12204-020-2238-4

• Computer & Communication Engineering • Previous Articles     Next Articles

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

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

LIU Ziwen (刘子文), XIAO Lei *(肖雷), BAO Jinsong (鲍劲松), TAO Qingbao (陶清宝)    

  1. (1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China;
    2. Mormount (Shanghai) Engineering Co., Ltd., Shanghai 203201, China)
  2. (1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China;
    2. Mormount (Shanghai) Engineering Co., Ltd., Shanghai 203201, China)
  • Online:2021-08-28 Published:2021-06-06
  • Contact: XIAO Lei *(肖雷) E-mail:leixiao211@dhu.edu.cn

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

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