Semi-supervised Bearing Fault Diagnosis with Adversarially-Trained Phase-Consistent Network

Abstract

In this study, we propose an adversarially-trained phase-consistent network (APCNet), which is a semi-supervised signal classification approach. The proposed classification model is trained with datasets that contain a small fraction of labeled output so as to design (1) an effective representation of the input time series (vibration signal) to extract important factors for the model to discriminate between different bearing conditions, and (2) a latent representation for the data to reflect the true data distribution precisely. To achieve these goals, APCNet suggests three novelties: the vibration-specific encoder, the phase-consistency regularization, and the adversarially-trained latent distribution alignment of the labeled and unlabeled distributions. We conduct experiments on two public bearing datasets and one public motor operating dataset to evaluate the performance of APCNet. We interpret the model's capabilities with different data label ratios and latent distribution analysis. The results show that APCNet performs well on datasets with small labeled to unlabeled data ratio. Also, we show that APCNet achieves our objectives of capturing important vibration signals features and modeling the true data distribution effectively.