Rolling Bearing Failure Feature Extraction Based on Large Parameters Stochastic Resonance ⋆

Based on rolling bearing fault signal modulation model in the process of spreading, an improved method that combining Hilbert envelop extraction algorithm and large parameter setting rules in stochastic resonance (SR) is proposed for features extraction. Firstly, Hilbert transform can effectively eliminate the interference of high frequency carrier signal. Secondly, parameters setting rules in a certain frequency range are summarized based on the simulation research on the realization of stochastic resonance under the condition of big parameters. Then, the improved method is used to deal with the experimental data of rolling bearing with typical faults. The experimental results show that the improved method can extract the fault feature and identify the fault type effectively