Fault Detection and Diagnosis Based on Modified Independent Component Analysis

Jong-Min LEE, S. Joe QIN*, In-Beum LEE

*Corresponding author for this work

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

403 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)3501-3514
Number of pages14
JournalAICHE Journal
Volume52
Issue number10
Early online date14 Sept 2006
DOIs
Publication statusPublished - Oct 2006
Externally publishedYes

Keywords

  • Fault detection
  • Fault diagnosis
  • Independent component analysis
  • Principal component analysis
  • Process monitoring

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