Fault Diagnosis Methods of Deep Convolutional Dynamic Adversarial Networks

Abstract

A DCDAN is proposed for intelligent fault diagnosis to address the issue that it is easy to obtain a large amount of labeled fault-type data in a laboratory environment but difficult or impossible to obtain a large amount of labeled data under actual working conditions. This method transfers the fault diagnosis knowledge acquired in the laboratory environment to the actual engineering equipment, obtains more comprehensive fault information by fusing the time domain and frequency domain data, employs the residual network to deeply extract fault features in the feature extraction layer, and makes use of the extracted fault features to improve fault diagnosis. To achieve unsupervised transfer learning, the marginal distributions and conditional probability distributions of the source and target domains are aligned by maximizing the domain classification loss, while the failure classification of mechanical equipment is achieved by minimizing the class prediction loss. The experimental results demonstrate that this model has a high classification accuracy in the unlabeled target data set and can effectively solve the problem of the lack of labels in the data set, i.e., realize intelligent mechanical fault diagnosis, under certain conditions.