Early Fault Diagnosis of Rolling Bearings Based on Parameter Optimization FMD
Since the early fault signal features of rolling bearing are weak,the performance of feature mode decomposition is affected by the length of parameter filter L and the number of modes n.A parametric opti-mization method for FMD early fault diagnosis is proposed.Firstly,the filter length L and the number of modes n of FMD are determined adaptively based on square envelope spectrum gini indix.Then,the signal is decomposed into n modal components by using parametric optimized FMD,and the sensitive modal com-ponents are selected according to the kurtosis value.Finally,the envelope analysis of the selected sensitive modal components is carried out to judge the fault type of the rolling bearing.Simulation and experimental results show that the proposed method can self-adaptively determine the optimal FMD parameter combina-tion and effectively extract fault feature information.Through comparison and analysis with variational mode decomposition,the fault feature frequency extracted by parameter optimization FMD is more obvious,and has better feature extraction performance,which can realize the accurate diagnosis of rolling bearing fault.
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