Motor Fault Diagnosis Method Based on Migration Learning and CNN
Aiming at the problem that the lack of labeled data will lead to poor training of convolutional neural network(CNN),a motor fault diagnosis method based on the combination of migration learning and CNN is proposed for three-phase asynchronous motor fault diagnosis.Firstly,an experimental platform for motor fault diagnosis is built,the label data of input CNN model is obtained by acceleration sensor,and the pre-trained model is obtained through training.Then,the obtained pre-training model is transferred to the target domain with transfer learning,and a small amount of labeled data in the target domain is cleared for training and fine-tuning parameters,and the CNN parameters are optimized by training the labeled data in the target domain.Finally,a new model with good classification ability for the target domain data is obtained,so as to realize the motor fault diagnosis in the case of scarce labeled data in the target domain.By comparing this method with ordinary CNN,variational modal decomposition(VMD)-support vector machine(SVM),VMD-K nearest neighbor(KNN)and VMD-BP neural network recognition models for validation,the results show that the pattern recognition method of migrating CNN model proposed in this paper has better recognition effect.
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