Signal Detection Based on Second-Order Underdamped Tristable Stochastic Resonance and Its Application to Weak Fault Diagnosis

The advantages of stochastic resonance (SR) have received extensive attention and research in the field of weak signal extraction. It can be used to extract fault signals from rotating machinery. To deeply improve the output signal-to-noise ratio (SNR), a second-order underdamped tristable stochastic resonance (SUTSR) is proposed in the present study. The potential function, the steady-state probability density of particles and the SNR are used to evaluate the model. Firstly, the relationship between the noise intensity and the SNR of SUTSR is studied. Then, using the steady-state solution curve, the output response of the SUTSR system is discussed from the perspective of steady-state input, and the output response of the system is further studied when inputting low-frequency harmonic signals. Finally, SUTSR model is used to process bearing signal data with inner ring fault and rolling element fault, and the processing result is compared with tristable system and second-order underdamped bistable system. The results show that, in the background of strong noise, the SUTSR system can accurately identify the characteristic frequency of the fault signal and then greatly improve the energy of the weak fault signal under appropriate system parameters.