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A Deep Transfer Learning Fault Diagnosis Method Based on WGAN and Minimum Singular Value for Non-Homologous Bearing

In real industry, due to changes in operating conditions and differences in systems of interest, domain shift is a common problem, which results in the degradation of the diagnostic performance. Moreover, insufficient labeled or unlabeled samples greatly limit the adaptability of fault diagnosis methods. To solve these problems, the latest domain adversarial transfer learning techniques are studied. However, most of the existing studies just reduce the distribution discrepancy of two domains with fixed distance metrics, which cannot adapt well to distribution variation during testing. This article proposes a new deep transfer learning method based on Wasserstein generative adversarial networks (WGAN) and minimum singular value for non-nomologous bearings, where the training samples and the testing samples are from related not the same machinery. In the proposed method, a domain critic network is adopted to provide the discrepancy metric for improving domain adaptation ability. In addition, the minimum singular value is used to capture effective categories information measurement in the classifier training process. The proposed method is verified by two different bearing datasets from Case Western Reserve University and the Intelligent Maintenance System. The experiments indicated that the proposed method can achieve superior performance over other existing methods in terms of diagnosis accuracy.