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Identification of faulty sensors using principal component analysis
Author(s) -
Dunia Ricardo,
Qin S. Joe,
Edgar Thomas F.,
McAvoy Thomas J.
Publication year - 1996
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690421011
Subject(s) - principal component analysis , identification (biology) , fault detection and isolation , fault (geology) , engineering , computer science , data mining , control theory (sociology) , artificial intelligence , control (management) , actuator , botany , seismology , biology , geology
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.