Multiple local domains transfer network for equipment fault intelligent identification. (April 2023)

• Record Type: Journal Article
• Authors: Wang, Yinjun; Ge, Liang; Xue, Chunrong; Li, Xiaobo; Meng, Xianghui; Ding, Xiaoxi
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Adversarial domain adaptation -- Convolutional neural network -- Equipment fault intelligent identification -- Global transfer learning -- Multiple local domain transfer learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105791
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Deep learning has been widely used in the field of mechanical fault diagnosis and equipment health monitoring. A crucial practical issue is the cross-domain machinery fault diagnosis, where we typically train the models with artificial fault samples designed from experimental simulation as the real fault samples are very limited in practice. The characteristic distribution of artificial fault samples in the laboratory is quite different from that of real fault samples in the industrial environment. Whereas, the conventional method mainly focuses on the adversarial domain transfer learning in global domain, ignoring the local similarity of the same fault types between source domain and target domain. This causes an incomplete transfer for the fault types distributed in two domains. To address this issue, this study proposes a multiple local domains transfer network, which consists of two feature extractors, a state classifier and multiple local domain discriminators. The multi-local domain adversarial learning can effectively reduce the negative transfer in network training. By comparing with other five cutting-edge deep learning models, the proposed method shows outstanding robustness and accuracy in fault intelligent identification. Especially, it can be also found that the average accuracy of the proposed method is respectively higher than that of other adversarial domain transfer methods with a promotion for 5% on bearing and gear datasets.