A novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification

Intelligent fault diagnosis based on deep neural networks and big data has been an attractive field and shows great prospects for applications. However, applications in practice face following problems. (1) Unexpected and unseen faults of machinery in real environment may be encountered. (2) Large collections of healthy condition samples and few fault condition samples result in the imbalanced distribution of machinery health conditions. This paper proposes a novel deep metric learning model, where machinery condition is classified by retrieving similarities. The trained deep metric learning model can learn and recognize new faults quickly and easily to address the first problem. As core of deep metric learning, a novel loss function called normalized softmax loss with adaptive angle margin (NSL-AAM) is developed for second problem. NSL-AAM can supervise neural networks learning imbalanced data without altering the original data distribution. Experiments for balanced and imbalanced fault diagnosis are conducted to verify the ability of the proposed model for fault diagnosis. The results demonstrate that the proposed model can not only extract more distinctive features automatically, but also balance the representation of both the majority and minority classes. Furthermore, the results of experiments for diagnosing new faults are reported, which proves the capability of the trained model for open-set classification. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel deep metric learning model to address imbalanced data distribution and quickly recognize new faults in fault diagnosis. Experimental results demonstrate that the proposed model can automatically extract distinctive features, balance data representation, and perform open-set classification effectively.