ROLLING BEARING WEAK FAULT FEATURE EXTRACTION BASED ON MULTIPOINT OPTIMAL MINIMUM ENTROY DECONVOLUTION ADJUSTED AND ADAPTIVE STOCHASTIC RESONANCE WITH CUCKOO SEARCH - QUAN ZhenYa; ZHANG XueLiang (Jixie qiangdu)
Adjusting the value of nonlinear system parameters will affect the output Signal-to-Noise Ratio（ SNR）,so Signalto-Noise Ratio（ SNR） was used as the output evaluation index of stochastic resonance. A method combining Multipoint Optimal Minimum Entroy Deconvolution Adjusted（ MOMEDA） with Cuckoo search adaptive Stochastic Resonance（ SR） was proposed to extract weak fault feature frequencies.Simulation analysis shows that using MOMEDA as the stochastic resonance pretreatment can significantly improve the weak fault signal,and experimental example verification further shows that the combination of MOMEDA method and stochastic resonance can effectively extract the characteristic frequency of weak fault signal from the signal with strong noise,so as to realize the weak fault diagnosis of rolling bearings.