Adaptive detection method of impact signal based on monostable stochastic resonance
                           WANG Hongtao，WANG Fengtao，XUE Yuhang，DENG Gang，LI Hongkun，HAN Qingkai
                                (School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract: Aiming at the problems of feature extraction of weak fault impact signal under strong noise background, the monostable stochastic resonance system and metrics were researched. Meanwhile, the fault diagnosis strategy of lowspeed slewing bearing was analyzed. An adaptive detection method of impact signal based on monostable stochastic resonance was proposed. The correlation of the parameters of the system was considered, the gray wolf optimization algorithm（GWO）was selected to optimize multiple parameters to achieve synchronization process. The weighted negative entropy index was constituted as the fitness function of GWO. The state monitoring and fault analysis between the simulated impact signal and the lowspeed slewing bearing signal were completed. The results indicate that it can easily and effectively use the noise energy to enhance the weak signal with fast convergence rate and ideal parameter optimization effect under strong noise background. This method can highlights the characteristics of the simulated impact signal and accurately diagnose the failure mode of the lowspeed slewing bearing, which has a good engineering application prospects in engineering practice.

Key words: monostable stochastic resonance; impact signal; adaptive; weighted negentropy index；grey wolf optimizer（GWO）