Optimal Transport-Based Deep Domain Adaptation Approach for Fault Diagnosis of Rotating Machine

Rotating machinery working under changing operation conditions is prone to failure. In recent years, domain adaptation has been successfully used for fault diagnosis. However, the existing fault diagnosis methods based on domain adaptation have two main disadvantages: 1) with these methods, it is difficult to precisely measure and estimate the differences between the source and target domains and 2) they only consider the discrepancies in the feature space, but not in the label space. In this article, a new optimal transport (OT)-based deep domain adaptation model is proposed for rotating machine fault diagnosis. The framework of the proposed method comprises three main components. First, an autoencoder network is designed to extract compact and class discriminative features from the raw data. Second, the domain-invariant representation features are trained by searching an OT plan with a predefined cost function between source and target domains and by minimizing the discrepancies of a joint distribution of the feature and label spaces based on OT. Finally, the classifier trained with data in the source domain is directly used to perform the classification task in the target domain. In addition, the optimal selection of the model hyper-parameters is verified through empirical analysis, and the transfer ability of the proposed model is visually illustrated in a reduced feature space. The experimental results show that the proposed method outperforms the existing machine learning and domain adaptation fault diagnosis methods, in terms of, e.g., classification accuracy and generalization ability.