Identification of faulty sensors using principal component analysis

Even though there has been a recent interest in the use of principal component analysis (PCA) for sensor fault detection and identification, few identification schemes for faulty sensors have considered the possibility of an abnormal operating condition of the plant. This article presents the use of...

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Bibliographic Details
Published in:AIChE journal 1996-10, Vol.42 (10), p.2797-2812
Main Authors: Dunia, Ricardo, Qin, S. Joe, Edgar, Thomas F., McAvoy, Thomas J.
Format: Article
Language:English
Subjects:
Applied sciences
Boilers
Chemical engineering
Exact sciences and technology
Mathematical models
Metrology, automation
Q1
Sensors
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Summary:Even though there has been a recent interest in the use of principal component analysis (PCA) for sensor fault detection and identification, few identification schemes for faulty sensors have considered the possibility of an abnormal operating condition of the plant. This article presents the use of PCA for sensor fault identification via reconstruction. The principal component model captures measurement correlations and reconstructs each variable by using iterative substitution and optimization. The transient behavior of a number of sensor faults in various types of residuals is analyzed. A sensor validity index (SVI) is proposed to determine the status of each sensor. On‐line implementation of the SVI is examined for different types of sensor faults. The way the index is filtered represents an important tuning parameter for sensor fault identification. An example using boiler process data demonstrates attractive features of the SVI.
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.690421011