Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

Yingwei ZHANG; S. Joe QIN

doi:10.1021/ie070381q

Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

Yingwei ZHANG^*, S. Joe QIN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

115 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 language	English
Pages (from-to)	7780-7787
Number of pages	8
Journal	Industrial and Engineering Chemistry Research
Volume	46
Issue number	23
Early online date	12 Oct 2007
DOIs	https://doi.org/10.1021/ie070381q
Publication status	Published - 7 Nov 2007
Externally published	Yes

Bibliographical note

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

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10.1021/ie070381q

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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.",

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N2 - 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.

AB - 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.

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Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

Abstract

Bibliographical note

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Correction to "Fault detection of nonlinear processes using multiway kernel independent component analysis" (Industrial and Engineering Chemistry Research (2007) 46:23 (7780-7787) DOI: 10.1021/ie070381q)

Cite this