Fault Diagnosis Method for Electric Multiple Units Bearings Based on Joint Moment Conditional Distribution Matching
It is difficult to learn a fault diagnosis model with high generalization performance due to the large operat-ing span of electric multiple units bearings,complex and variable working conditions,high safety requirements and different data distribution.In order to overcome the problem of decreasing model generalization performance caused by the difference of data distribution and improve the ability of model cross-working condition diagnosis,a domain adaptive method based on joint moment condition distribution matching is proposed for cross-working condition fault diagnosis of electric multiple units bearings.The basic idea of this method is to couple the category information of input data into extracted feature representation through the linear mapping method of feature space,so as to improve the ability of feature to express data characteristics.Based on features that incorporate conditional distribution informa-tion,the index(joint moment)of the combined first and second order statistics is constructed to enhance the description of the data distribution.Meanwhile,the feature matching based on the joint moment is carried out to align the domain distribution,and promote the model to learn the shared feature representation and the transfer of diagnos-tic knowledge.The experimental results of cross-working fault diagnosis cases based on two public rolling bearing data sets show that the proposed method has more effective fault feature learning ability and knowledge transfer per-formance than other methods,and can obtain the best diagnosis results on multiple tasks.
electric multiple unitsrolling bearingsfault diagnosistransfer learningstatistical moment matching
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