Improved principal component monitoring using the local approach |
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Authors: | Uwe Kruger [Author Vitae] Sukhbinder Kumar [Author Vitae] |
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Affiliation: | a School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5AH, UK b School of Neurology, Neurobiology and Psychiatry, University of Newcastle upon Tyne, NE2 4HH, UK |
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Abstract: | This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. |
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Keywords: | Principal component analysis Dynamics Local approach Fault detection and isolation Covariance structure |
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