期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 26, 期 3, 页码 1591-1601出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2020.3025615
关键词
Feature extraction; Fault diagnosis; Fault detection; Machinery; Training; Task analysis; Knowledge transfer; Adversarial learning strategy; deep transfer learning; fault diagnosis; rotating machinery; unsupervised learning
类别
资金
- National Key Research and Development Program of China [2018YFB1702400]
- National Natural Science Foundation of China [51875208, 51705156]
Research on intelligent fault diagnosis based on deep transfer learning has led to the proposal of a two-stage transfer adversarial network for rotating machinery, which can effectively separate new fault types and recognize the quantity. Experimental results indicate that the proposed scheme can address fault diagnosis transfer tasks with multiple new faults in the target domain.
Recently, deep transfer learning based intelligent fault diagnosis has been widely investigated, and the tasks that source and target domains share the same fault categories have been well addressed. However, due to complexity and uncertainty of mechanical equipment, unknown new faults may occur unexpectedly. This problem has received less attention in the current research, which seriously limited the application of deep transfer learning. In this article, a two-stage transfer adversarial network is proposed for multiple new faults detection of rotating machinery. First, a novel deep transfer learning model is constructed based on an adversarial learning strategy, which can effectively separate multiple unlabeled new fault types from labeled known ones. Second, an unsupervised convolutional autoencoders model with silhouette coefficient is built to recognize the number of new fault types. Extensive experiments on a gearbox dataset validate the practicability of the proposed scheme. The results suggest that it is promising to address fault diagnosis transfer tasks in which the multiple new faults occur in the target domain, which greatly expand the application of deep transfer learning.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据