On the Motor Fault Diagnosis Method Based on Multi-channel Data Fusion and CNN
In order to solve the problem of difficult motor fault diagnosis caused by complex motor structures and non-station-ary signals,as well as the dependence of traditional fault diagnosis algorithms on expert experience,a fault diagnosis method for motors based on multi-channel data fusion and convolutional neural networks(CNN)was proposed.The method first collects vi-bration signals and stator current signals at the motor drive end and converts them into frequency domain signals,then normalizes the frequency domain signals of the two signals and converts them into two-dimensional spectrum data.Finally,a CNN network model is constructed,the hyperparameter such as network layers and learning rate are determined,and samples are input into the model for fault feature extraction and classification diagnosis.The results showed that the accuracy of motor fault diagnosis using this method under appropriate parameters is100%.Compared with traditional fault diagnosis methods and the1D-CNN model u-sing vibration or current signals alone,this method can more effectively diagnose various motor faults.
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