Early warning of slight changes in systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Dynamic principal component analysis using subspace model identification
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
Fault detection in multivariate signals with applications to gas turbines
IEEE Transactions on Signal Processing
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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.