Fault Detection and Isolation with Robust Principal Component Analysis

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 was 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 a robust PCA model that could be used for outliers detection and isolation. Hence a scale-M estimator [1] is used to determine a robust model. This estimator is computed using an iterative re-weighted least squares (IRWLS) procedure. This algorithm is initialized from a very simple estimate derived from a one-step weighted variancecovariance estimate [2]. 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 resid...

• Type: Journal
• Year: 2008
• Where: AMCS
• Authors: Yvon Tharrault, Gilles Mourot, José Ragot, Didier Maquin
• Keywords: AMCS 2008, Mathematics, Multiple Faults, Outliers, Principal Component Analysis