Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

• Published in: Industrial & engineering chemistry research Vol. 46; no. 23; pp. 7780 - 7787
• Main Authors: Zhang, Yingwei, Qin, S. Joe
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 07.11.2007
• Subjects: Applied sciences; Chemical engineering; Exact sciences and technology; Safety; Non linear system; Failure detection; Independent component analysis
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/ie070381q

In this paper, a new nonlinear process monitoring method that is based on multiway kernel independent component analysis (MKICA) is developed. Its basic idea is to use MKICA to extract some dominant independent components that capture nonlinearity from normal operating process data and to combine them with statistical process monitoring techniques. The proposed method is applied to the fault detection in a fermentation process and is compared with modified independent component analysis (MICA). Applications of the proposed approach indicate that MKICA effectively captures the nonlinear relationship in the process variables and show superior fault detectability, compared to MICA.