Abstract
A method of determining model dimensions (number of principal components) which maximize the sensitivity of fault detection was studied. In this paper it is shown that the sensitivity of PCA-based fault detection generally depends on the number of principal components. Most of the existing methods give only one value as a recommended number of components, and so sometimes the sensitivity is poor for certain kinds of faults. Among existing methods, although the Variance of Reconstruction Error (VRE) criterion gives a recommended model dimension which depends on the kind of fault, it is not intended to maximize sensitivity. This paper presents a new method of determining the model dimension which maximizes the sensitivity of PCA-based fault detection
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