Reconstruction-Based Contribution for Process Monitoring with Kernel Principal Component Analysis

Carlos F. ALCALA, S. Joe QIN*

*Corresponding author for this work

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

188 Citations (Scopus)

Abstract

This paper presents a new method for fault diagnosis based on kernel principal component analysis (KPCA). The proposed method uses reconstruction-based contributions (RBC) to diagnose simple and complex faults in nonlinear principal component models based on KPCA. Similar to linear PCA, a combined index, based on the weighted combination of the Hotelling's T2 and SPE indices, is proposed. Control limits for these fault detection indices are proposed using second-order moment approximation. The proposed fault detection and diagnosis scheme is tested with a simulated CSTR process where simple and complex faults are introduced. The simulation results show that the proposed fault detection and diagnosis methods are effective for KPCA. © 2010 American Chemical Society.
Original languageEnglish
Pages (from-to)7849-7857
Number of pages9
JournalIndustrial and Engineering Chemistry Research
Volume49
Issue number17
Early online date30 Mar 2010
DOIs
Publication statusPublished - 1 Sept 2010
Externally publishedYes

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