Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

Yingwei ZHANG*, S. Joe QIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

114 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)7780-7787
Number of pages8
JournalIndustrial and Engineering Chemistry Research
Volume46
Issue number23
Early online date12 Oct 2007
DOIs
Publication statusPublished - 7 Nov 2007
Externally publishedYes

Bibliographical note

This work was supported the Texas-Wisconsin Modeling and Control Consortium.

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