Coupled Hybrid Stochastic Resonance With Multiobjective Optimization for Machinery Dynamic Signature and Fault Diagnosis

Most traditional stochastic resonance methods pose three fundamental limitations in extracting fault signature: 1) simulated impulse signals are insufficient to match the complicated mechanical vibration signals; 2) 1-D signals are vulnerable to interference from multiscale noise, which potentially yields false detection; and 3) optimization function in the signal-to-noise ratio (SNR) largely depends on the advanced requirements of the characteristic frequency, thereby restricting the development for actual operation. Accordingly, this study investigated the dynamic model of rolling bearings and explored the benefits of the coupled hybrid stochastic resonance (CHSR) method for identifying weak signal signatures. In addition, we proposed multiobjective optimization and established a dynamic model of a rolling bearing with four degrees of freedom under a rotor system with external excitation. Overall, the proposed system offers three main advantages: 1) the dynamic model facilitates correspondence with real vibration signals; 2) the CHSR method can process 2-D signals and overcome the saturation phenomenon, which enhances the weak useful signal signature describing machinery health; and 3) the cross correlation spectral kurtosis (CSK) and the critical amplitude are defined within the multiobjective function to adaptively optimize the parameter adjustment of the CHSR. Thereafter, the proposed method exhibited adequate enhancement ability integrated the study on dynamic simulation and machinery bearings of wind power generator. Moreover, the applied machine learning model further clarified the superior performance of CHSR method in identifying the weak fault signature.