A nonlinear quality-related fault detection approach based on modified kernel partial least squares. (January 2017)

• Record Type: Journal Article
• Authors: Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen
• Is Part Of: ISA transactions. Volume 66(2017:Jan.)
• Page Start: 275
• Page End: 283
• Publication Date: 2017-01
• Subjects: Data-driven -- Quality-related -- Fault detection -- Kernel partial least squares -- Singular Value Decomposition -- Nonlinear monitoring; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.10.015
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Highlights: The proposed method maps original process variables space into feature space to deal with nonlinearities.. A KPLS model is used to build the linear relationship between kernel and output matrices. The kernel matrix is decomposed into two orthogonal parts by singular value decomposition. The statistics for each parts are determined appropriately for the purpose of quality-related fault detection. The proposed method has a more simple diagnosis logic and more stable performance.