Fault detection using robust multivariate control chart |
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| Authors: | George Stefatos A Ben Hamza |
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| Affiliation: | 1. Pratt & Whitney, Longueuil, QC, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, 1515 Ste-Catherine Street West, Montréal, QC, Canada H3G 2W1;1. PIMM Laboratory, UMR CNRS 800, Arts et Métiers ParisTech, Paris, France;2. LAMIH Research Center UMR CNRS 8201, UVHC, Le Mont Houy, F-59313 Valenciennes, France;3. CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia |
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| Abstract: | We introduce a new multivariate statistical process control chart for fault detection using robust statistics and principal component analysis. The proposed approach consists of two main steps. In the first step, a robust covariance matrix is determined using the minimum covariance determinant algorithm. In the second step, an eigen-analysis of the robust correlation matrix is performed to derive the robust control limits of the proposed multivariate chart. Our experimental results illustrate the much better fault detection performance of the proposed method in comparison with existing statistical monitoring and process controlling charts. |
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