Deep Coupled Joint Distribution Adaptation Network: A Method for Intelligent Fault Diagnosis Between Artificial and Real Damages

Abstract

Deep learning techniques have been widely applied for intelligent fault diagnosis. However, these techniques require large amounts of labeled data from a particular machine, which is demanding for real-world applications. Alternatively, models can be developed based on artificial damages and be applied for industrial data with real damages. In that case, a major challenge arises since the distributions of those artificial and real damages are greatly different, which results in severe performance degradation of conventional deep models. In this work, a model named deep coupled joint distribution adaptation network (DCJDAN) is proposed to address the large domain discrepancy between artificial and real damages. By utilizing two untied deep convolutional networks, the proposed method allows the source- and target-stream networks to focus on learning domain-representative features, providing flexibility for explicitly modeling the domain discrepancy. To ensure a more effective knowledge transferring, a regulation term is adopted to force the untied coupled networks to stay similar since the source domain and the target domain are related. The joint distribution adaptation module is further adapted to minimize the domain discrepancy, which considers both the marginal and conditional distribution differences and provides more accurate distribution matching. The effectiveness of the proposed method is evaluated based on three bearing data sets with artificial and real damages. As reported, the proposed method achieves an average accuracy of 98.17% for all tasks, which outperforms several state-of-the-art deep domain adaptation models and improves the diagnosis performance compared with the conventional deep learning models.