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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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
- stochastic resonance (SR)
- fault detection
- triple-well potential system
- particle swarm optimization
- TP 391.7