Fault detection and diagnosis based on modified independent component analysis

A novel multivariate statistical process monitoring (MSPM) method based on modified independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to extract statistically independent components from observed data, which has drawn considerable attention in research fields s...

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Bibliographic Details
Published in:AIChE journal 2006-10, Vol.52 (10), p.3501-3514
Main Authors: Lee, Jong-Min, Qin, S. Joe, Lee, In-Beum
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Chemical engineering
Exact sciences and technology
fault detection
Fault diagnosis
General purification processes
independent component analysis
Pollution
principal component analysis
process monitoring
Safety
Statistical analysis
Wastewaters
Water treatment
Water treatment and pollution
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Summary:A novel multivariate statistical process monitoring (MSPM) method based on modified independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to extract statistically independent components from observed data, which has drawn considerable attention in research fields such as neural networks, signal processing, and blind source separation. In this article, some drawbacks of the original ICA algorithm are analyzed and a modified ICA algorithm is developed for the purpose of MSPM. The basic idea of the approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. Variable contribution plots to the monitoring statistics (T2 and SPE) are also developed for fault diagnosis. The proposed monitoring method is applied to fault detection and diagnosis in a wastewater treatment process, the Tennessee Eastman process, and a semiconductor etch process and is compared with conventional PCA monitoring methods. The monitoring results clearly illustrate the superiority of the proposed method. © 2006 American Institute of Chemical Engineers AIChE J, 2006
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.10978