Deep domain adversarial method with central moment discrepancy for intelligent transfer fault diagnosis. (14th September 2021)

• Record Type: Journal Article
• Authors: Xu, Kun; Li, Shunming; Li, Ranran; Lu, Jiantao; Zeng, Mengjie
• Is Part Of: Measurement science & technology. Volume 32:Number 12(2021)
• Publication Date: 2021-09-14
• Subjects: fault diagnosis -- transfer fault -- adversarial learning -- central moment discrepancy -- variable speed; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac20f1
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Big data condition monitoring in the industrial of internet era is indispensable, and intelligent fault diagnosis plays an important role in it. The adversarial learning method is widely used because of its ability to extract domain invariant features to solve the variable speed fault diagnosis problem. However, its training process is often unstable and difficult to converge to the optimal solution, which brings great challenges to the fault detection of equipment. In view of this exasperating problem, a novel model, called deep domain adversarial method with central moment discrepancy, is proposed. The presented model mainly consists of four modules: a shared weight feature extraction network with wide convolution kernel, a supervised classification network, an adversarial domain classification network, and a CMD alignment network. Adversarial domain classification network is employed to extract features that have both category distinction and domain invariance in the process of mutual game learning between features of source domain and target domain. The CMD alignment network can be devoted to align the higher-order moments of two domain features to constrain the instability in adversarial learning. Through the above regularization method, the model shows a relatively stable and higher accuracy of transferring diagnosis in the non-standardized data. The public test data set and the private data set are applied to validate the model. The results show that the proposed model successfully solves the problem of training instability in adversarial learning and has a relatively high diagnostic accuracy.