In complex and harsh environment,it is difficult to extract the fault information of rolling bearings accurately.Therefore,multi-scale mathematical morphology is used to study the fault diagnosis of rolling bearings.Since small-scale morphological filtering can better preserve signal details,and large-scale morphological filtering can effectively suppress noise,in order to better balance noise suppression and fault feature information preservation,the iterative threshold method is used to select the scale range,the frequency domain kurtosis method is used to calculate the threshold value,and then the optimal scale interval is obtained through iterative self-adaptation.The multi-scale morphological signal reconstruction weighting method uses the weighted multi-scale morphological gradient algorithm,which can ensure that the small scale has a small weight and the large scale has a large weight.The simulation and experiment show that the multi-scale morphology can effectively detect the fault signal of the rolling bearing,and the fault characteristic information of the rolling bearing can be deeply mined.
维普
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