Fault detection and isolation with robust principal component analysis

• Autorzy: Yvon Tharrault , Gilles Mourot , José Ragot , Didier Maquin
• Języki publikacji: EN

Abstrakty

EN

Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a “local” matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.

Słowa kluczowe

EN

principal component analysis; robustness; outliers; fault detection and isolation; structured residual vector; variable reconstruction

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 4
• Strony: 429-442

Daty

wydano

2008

otrzymano

2007-12-23

poprawiono

2008-06-04

poprawiono

2008-06-13

Twórcy

autor

Yvon Tharrault

• Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, Nancy Université, CNRS, 2, Avenue de la forêt de Haye, F-54 516 Vandoeuvre-lès-Nancy, France

autor

Gilles Mourot

• Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, Nancy Université, CNRS, 2, Avenue de la forêt de Haye, F-54 516 Vandoeuvre-lès-Nancy, France

autor

José Ragot

• Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, Nancy Université, CNRS, 2, Avenue de la forêt de Haye, F-54 516 Vandoeuvre-lès-Nancy, France

autor

Didier Maquin

• Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, Nancy Université, CNRS, 2, Avenue de la forêt de Haye, F-54 516 Vandoeuvre-lès-Nancy, France

Bibliografia

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Identyfikatory

DOI

10.2478/v10006-008-0038-3