Study on Weak Signal Feature Extraction Based on Asymmetric Hybrid Bistable Stochastic Resonance

Aiming at the traditional stochastic resonance system's difficulty in extracting the weak signal fault eigenfrequency in the strong noise background, an asymmetric hybrid bistable stochastic resonance system (AHBSR system) model is proposed based on the hybrid bistable system model. First, the asymmetric factor is introduced to improve the hybrid bistable model, the AHBSR system model is constructed, and the system is solved numerically based on the fourth-order Lunger-Kutta algorithm to analyze the effect of noise intensity on the system. Second, the output signal-to-noise ratio (SNR) is used as a measure of the stochastic resonance effect, and the particle swarm algorithm is used to optimize the system parameters in combination with the quadratic sampling technique, and the system outputs are compared and analyzed with those of the hybrid bistable model and the classical asymmetric bistable model, which proves that the extraction of the characteristic frequencies of the weak-signal faults by the AHBSR model is more accurate, and the detection deviations are all within 0.4 Hz, which is lower than those of the other two kinds of bistable systems. Compared with the original signals, the output SNRs are increased by more than 30 dB, which is higher than the other two bistable systems, and the amplitudes corresponding to the eigenfrequencies are amplified by more than 700\times , moreover, the AHBSR system is capable of extracting the composite fault signature frequency that cannot be recognized by the other two bistable systems. Finally, given the problem that the SNR index needs to predict the exact fault characteristic frequency when calculating, the weighted kurtosis coefficient is proposed as a measure of the stochastic resonance effect, which is verified by signal simulation and experimental datasets of bearings with different fault types, to prove the accuracy of the AHBSR system in identifying and extracting the fault characteristics of the bearing signals, as well as the validity of the weighted kurtosis coefficient index.