Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 46, Issue 23, Pages 7780-7787Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ie070381q
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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 delectability, compared to MICA.
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