自构建关联噪声下的随机共振及其在故障诊断上的应用

Rolling element bearings are the crucial component of rotating machine, timely health monitoring can effectively prevent the breakdown of the machine, further reduce economic losses. Here, firstly, this paper proposes a stochastic resonance system driven by self-constructingly correlated noise (DSCSR), and theoretically analyzes the signal-to-noise ratio (SNR). The theoretical analysis shows that stochastic resonance can be observed by adjusting the parameters of this nonlinear system. Secondly, aiming at the limitation of requiring accurate prior knowledge when using stochastic resonance phenomenon for fault diagnosis, the SNR evaluation index based on power spectrum is further proposed to determine the optimal system parameters when stochastic resonance occurs in the nonlinear system. Power spectral analysis is performed on the output signals of the optimal parametric system to determine the fault types. Finally, the effectiveness of the proposed method is validated using bearing fault diagnosis experiment and actual examples of fan' s bearing inner race fault, and its ability to enhance weak fault features and suppress the interferences of other harmonics and random noise is also verified.