Use of Principal Component Analysis for Sensor Fault Identification

Ricardo DUNIA*, S. Joe QIN, Thomas F. EDGAR, Thomas J. MCAVOY

*Corresponding author for this work

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

69 Citations (Scopus)


This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.
Original languageEnglish
Pages (from-to)S713-S718
Number of pages6
JournalComputers and Chemical Engineering
Issue numberSuppl. 1
Publication statusPublished - 1996
Externally publishedYes


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