Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning

Motor fault diagnosis based on deep learning frameworks has gained much attention from academic research and industry to guarantee motor reliability. Those methods are commonly under two default assumptions: 1) massive labeled training samples and 2) the training and test data share a similar distribution under unvarying working conditions. Unfortunately, these assumptions are nearly invalid in a real-world scenario, where the signals are unlabeled and the working condition changes constantly, resulting in the diagnosis models of the previous studies that always fail in classifying the unlabeled data in real applications. To deal with those issues, in this paper, we propose a novel feature adaptive motor fault diagnosis using deep transfer learning to improve the performance by transferring the knowledge learned from labeled data under invariant working conditions to the unlabeled data under constantly changing working conditions. A convolutional neural network (CNN) is adopted as the base framework to extract multi-level features from raw vibration signals. Then, the regularization term of maximum mean discrepancy (MMD) is incorporated in the training process to impose constraints on the CNN parameters to reduce the distribution mismatch between the features in the source and target domains. To verify the effectiveness of our proposal, data from the motor tests of European driving cycle (NEDC) for simulating the real working scenario and the motor tests under invariant working conditions are, respectively, conducted as the target domain and the source domain. The results show that the proposal presents higher diagnosis accuracy for the unlabeled target data than other methods, and it is of applicability to bridge the discrepancy between different domains.