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 reconstmction. 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 (SW) 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 SW.

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A rigorous and generally applicable method of deriving dynamic equations for pH in controlled stirred tank reactors (CSTRs) is presented. A specific example of neutralizing sodium hydroxide with acetic acid is discussed in detail. Experimental results on a laboratory-sized CSTR verified the accuracy of the derived model. A companion paper which follows describes the results of control studies on the sodium hydroxide-acetic acid CSTR.