Improved principal component monitoring using the local approach.

*(English)*Zbl 1126.62122Summary: 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.

##### MSC:

62P30 | Applications of statistics in engineering and industry; control charts |

62H25 | Factor analysis and principal components; correspondence analysis |

##### Keywords:

principal components analysis; dynamics; fault detection and isolation; covariance structure
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\textit{U. Kruger} et al., Automatica 43, No. 9, 1532--1542 (2007; Zbl 1126.62122)

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