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Fault detection and diagnosis based on modified independent component analysis
Author(s) -
Lee JongMin,
Qin S. Joe,
Lee InBeum
Publication year - 2006
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.10978
Subject(s) - independent component analysis , fault detection and isolation , process (computing) , blind signal separation , principal component analysis , computer science , pattern recognition (psychology) , multivariate statistics , component (thermodynamics) , artificial neural network , fault (geology) , artificial intelligence , statistical process control , data mining , engineering , machine learning , operating system , computer network , channel (broadcasting) , physics , seismology , actuator , thermodynamics , geology
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 (T 2 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