Fault Feature Extraction Method for Rolling Bearings based on Adaptive Variational Mode Decomposition
Aiming at the problem that rolling bearing weak fault characteristics are easily submerged by noise and strong fault components leading to the missed diagnosis or misdiagnosis,based on the mutual information and information entropy,the multi-objective fitness function formed by the diagnosis oriented adaptive variational mode decomposition(DOA-VMD)algorithm for fault diagnosis can effectively extract information to convey fault features without generating abnormal modal interference;and the NSGA-Ⅱ al-gorithm is used to search for the optimal Pareto solution set for the multi-objective fitness function;then,with considering the kurtosis is effective indicator to reflect the conflict,and the maximum kurtosis value is taken as the target to filter the optimal results in the solution set for the determination of DOA-VMD pa-rameters and feature extraction;the reliability of the proposed method is verified based on the gearbox bearing inner ring damage data.Results show that DOA-VMD can eliminate the noisy components and re-tain the features with the most significant impact signals,and the features can highlight the fault feature frequency better than the traditional VMD method.
rolling bearingmutual informationinformation entropyvariational mode decomposition(VMD)
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