Abstract
This paper presents the use of Principal Component Analysis (PCA) for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable by using iterative substitution and optimization. The effect of different sensor faults on model based residuals is analyzed and a new indicator called the Sensor Validity Index (SVI) is defined to determine the status of each sensor. An example using boiler process data demonstrates the attractive features of the SVI.
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