ITD结合参数优化MOMEDA的滚动轴承故障特征提取OA北大核心CSTPCD

Aiming at the problemthat the intrinsic time scale decomposition(ITD)method is difficult to extract bearing fault features under the influence of strong background noise,a new fault features extraction method for rolling bearings combining ITD and parameter optimized multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)is proposed.First,the ITD component containing rich fault information is extractedfrom fault signals according to the principle of maximum crest factor of envelope spectrum.Then,the MOMEDA noise reduction process is performed on the decomposedcomponent.The two parameters-fault period T and filter length L that affect the filtering effect of MOMEDA,are optimized with multi-point kurtosis and Gini index of square envelope spectrum respectively.Finally,envelope spectrum analysis is performed to extract fault characteristic frequencies.The analysis of the simulated signal and the measured signal shows that the new method can effectively extract the fault features of rolling bearings under the strong noise interference.