Use of principal component analysis for sensor fault identification

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...

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Bibliographic Details
Published in:Computers & chemical engineering 1996, Vol.20, p.S713-S718
Main Authors: Dunia, Ricardo, Joe Qin, S., Edgar, Thomas F., McAvoy, Thomas J.
Format: Article
Language:English
Subjects:
Applied sciences
Chemical engineering
Exact sciences and technology
Metrology, automation
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Summary: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.
ISSN:0098-1354
1873-4375
DOI:10.1016/0098-1354(96)00128-7