Stochastic resonance in strong Poisson white noise excited system and its application in multi-features identification

The response of a nonlinear system excited by multi-frequency signals and Poisson white noise contains abundant dynamic behaviours. The study on system response has certain practical values. Utilising optimal dynamics response has unique advantages in weak feature identification under strong background noise. In this work, we study the optimal dynamics response in the bistable system excited by multi-frequency signals and Poisson white noise. The effects of noise parameters on system response and feature identification are discussed and analysed. Also, a signal identification method for unknown multi-frequency signals based on the optimal resonance response of the nonlinear system is proposed and applied to the compound fault diagnosis of rolling bearings, which utilises the difference between stochastic resonance and coherence resonance to determine characteristic components of the signals. The proposed method effectively identifies the characteristic components of the original excitation and avoids misjudgment caused by strong noise, which is effectively verified via simulation analysis and experiments.