Stochastic resonance in an underdamped Duffing bistable system embedded with overdamped feedback control for rolling bearing fault detection

Research shows that multi-system synergetic stochastic resonance (SR) can obtain preferable performance for weak signal detection than a single SR system. However, how to design or select suitable multi-system synergetic mode remains a challenge. Therefore, an underdamped Duffing bistable SR system embedded with overdamped feedback control is constructed to boost the poor detection effect of weak signals by a single resonant system. Firstly, SR phenomenon in the underdamped Duffing bistable system embedded with overdamped feedback control is analyzed. Through the information exchange between the two subsystems and the series-parallel relationship of multiple integrals, it is feasible to enhance the noise utilization as well as to obtain a larger output signal-to-noise ratio. Secondly, the effects of feedback and damping coefficients on the resonant system are investigated, and an adaptive multi-parameter optimization SR algorithm is put forward in combination with gray wolf optimization algorithm to detect weak signals in a highly noisy background. Finally, vibration data of two faulty bearings is applied in the analysis of the investigated algorithm, and the results of comparison show that the spectral peak corresponding to the characteristic frequency of the output signal derived from the investigated method is more prominent, which effectively promotes the detection performance of bearing faults.