A complex process fault diagnosis method based on manifold distribution adaptation. (January 2020)

• Record Type: Journal Article
• Authors: Wang, Xiaogang; Zhao, Jianwei
• Is Part Of: Engineering applications of artificial intelligence. Volume 87(2020)
• Publication Date: 2020-01
• Subjects: Grassmann manifold -- Dynamic distribution adaptation -- Instance reweighting -- Domain adaptation; 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.2019.103267
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The main challenge for complex industrial processes is that the distribution of new process data is different from that of old process data, resulting in poor performance when monitoring the new process (target domain) using the model established by the old process (source domain) data. Moreover, new process data is often unlabeled, which is more common in actual industrial processes. Therefore, this paper proposes a novel fault diagnosis method for complex industrial processes based on Manifold Distribution Adaptation (MDA) to cope with this challenge. Specifically, in order to avoid feature degradation caused by feature transformation directly in the original feature space, MDA first maps source domain data and target domain data to Grassmann manifold through geodesic flow kernel. Second, dynamic distribution adaptation and source domain instance re-weighting are performed simultaneously on the Grassmann manifold to reduce the shift between the domains. Then, the base classifier trained by the adaptive source domain data can achieve accurate classification of the target domain data. Finally, the superiority of MDA is verified on the public transfer learning datasets and the actual industrial process datasets.