TY - GEN
A1 - Tharrault, Yvon
A1 - Mourot, Gilles
A1 - Ragot, José
A1 - Maquin, Didier
A2 - Korbicz, Józef - ed.
A2 - Sauter, Dominique - ed.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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).
N2 - 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.
L1 - http://zbc.uz.zgora.pl/Content/78731/AMCS_2008_18_4_1.pdf
L2 - http://zbc.uz.zgora.pl/Content/78731
KW - principal component analysis (PCA)
KW - robustness
KW - outliers
KW - fault detection and isolation
KW - structured residual vector
KW - variable reconstruction
T1 - Fault detection and isolation with robust principal component analysis
UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=78731
ER -