Fault detection and isolation with robust principal component analysis - Tharrault, Yvon; Mourot, Gilles; Ragot, José; Maquin, Didier (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: principal component analysis (PCA); robustness; outliers; fault detection and isolation; structured residual vector; variable reconstruction