A Adaptive Stochastic Resonance Method Based on Two-Dimensional Tristable Controllable System and Its Application in Bearing Fault Diagnosis

At present, most systems with good performance have more parameters. However, increasing the number of parameters lead to increasing the difficulties of parameter optimization, thus reducing the system's feasibility. To solve this problem, a model of controllable coupled stochastic resonance system is proposed. Firstly, the formula of the output signal-to-noise ratio of the model is derived and analyzed. Then the accuracy of the formula's derivation and results is proved by numerical simulation. It provides a theoretical basis for adjusting the parameters and coupled coefficients of the control system to induce a stochastic resonance in the controlled system or make it much stronger. Finally, better system parameters are obtained by genetic algorithm for the controlled system (tristable system), and then better system performance is obtained by adjusting the parameters and coupled coefficient of the control system (monostable system). The model is applied into the bearing fault detection and the results show that the model achieves better performance without increasing the complexity of parameter optimization, and has great practical value in weak signal detection.