An improved PCA scheme for sensor FDI: Application to an air quality monitoring network |
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Authors: | Mohamed-Faouzi Harkat Gilles Mourot Jos Ragot |
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Affiliation: | aUniversité Badji Mokhtar, ANNABA, Faculté des Sciences de l’Ingénieur, Département d’Électronique, BP. 12, Sidi Amar, 23000 Annaba, Algérie;bInstitut National Polytechnique de Lorraine, Centre de Recherche en Automatique de Nancy, UMR-CNRS INPL/UHP 7039, 2, Avenue de la Forêt de Haye 54516 Vandoeuvre-lès-Nancy, France |
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Abstract: | In this paper a sensor fault detection and isolation procedure based on principal component analysis (PCA) is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes. |
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Keywords: | Fault diagnosis Principal component analysis Sensor failure detection and isolation Variable reconstruction Air quality monitoring network |
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