Bearing fault signal detection based on whale algorithm to optimize stochastic resonance parameters

Aiming at the problem that the traditional stochastic resonance system cannot adaptively adjust the structural parameters in the bearing fault signal detection, this paper proposes a parameter adaptive stochastic resonance bearing fault signal detection method based on the whale optimization algorithm. The method is based on the bistable stochastic resonance system model, and the structural parameters of the bistable stochastic resonance are optimized through the adaptive whale algorithm, so that the fault signal is enhanced, and the bearing fault signal detection is realized. The method was verified by actual bearing fault signals, and a series of comparative experiments were carried out. The results show that the method in this paper has a simple model, few algorithm parameters, fast convergence speed, and a large output signal-to-noise ratio of the stochastic resonance system, which can accurately and efficiently detect bearing fault signals.