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Determination of principal component analysis models for sensor fault detection and isolation

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  • Control Applications
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Abstract

In this paper, a new method for determining the Principal Component Analysis (PCA) model structure for system diagnosis is proposed. This method, based on the variables reconstruction principle, determines the PCA model optimizing detection and isolation of single or multiple faults affecting redundant or non redundant variables of a system. This new method has been validated by a simulation example.

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Authors and Affiliations

  1. Anissa Benaicha is with the Research Unit ATSI, National Engineering School of Monastir, Rue Ibn El Jazzar, 5019, Monastir, Tunisia

    Anissa Benaicha

  2. Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, University of Lorraine, CNRS, 2, avenue of Forêt of Haye, 54516, Vandoeuvre-Lès Nancy, France

    Gilles Mourot & José Ragot

  3. Research Unit LARA Automatic, National Engineering School of Tunis, BP 37, 1002, Tunis Belvédère, Tunisia

    Kamel Benothman

Authors
  1. Anissa Benaicha
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  2. Gilles Mourot
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  3. Kamel Benothman
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  4. José Ragot
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Corresponding author

Correspondence to Anissa Benaicha.

Additional information

Recommended by Editorial Board member Bin Jiang under the direction of Editor Myotaeg Lim.

Anissa Benaicha received her engineering degree in Electric in 2007 and her MSc degree in Automatics and Industrial Maintenance in 2008 from the National Engineering School of Monastir, Tunisia. She is currently a Ph.D. student in the same university. Her interests include fault diagnosis, multi statistical process control, and interval arithmetic.

Gilles Mourot received his Ph.D. in Electrical Engineering in 1993 from the “Institut National Polytechnique de Lorraine (INPL)”, Nancy, France. His research activities, within Automatic Control Research Center of Nancy (CRAN), include data-driven fault detection and isolation and multiple model identification of nonlinear system.

Kamel Benothman received the license in Mechanical and Energetic Engineering in 1980 from the University of Valencienne, France. He obtained his engineering diploma in Mechanics and Energetic, an MSc degree in Automatics and Signal Processing in 1981 and a Ph.D. in Automatics and Signal Processing in 1984 from the same University. He received his DSc degree from the National Engineering School of Tunis, Tunisia in 2008. He is currently a professor at the High Institute of Sciences and Energy Technologies of Gafsa, Tunisia. His interests include reliability, fault diagnosis, multi statistical process control, and fuzzy systems. He is a member of the Association of Electrician Specialists in Tunisia ASET.

José Ragot received the Engineer’s degree with specialization in Control from the Ecole Centrale de Nantes (France) in 1969. Then, he joined the University of Nancy (France) where in 1973 he obtained his Ph.D. degree, a position as assistant professor at University Henri Poincaré in Nancy, and in 1980 the “Diplôme de Doctorat-es-Sciences”. José Ragot moved up to professor in 1985 at the Institut National Polytechnique de Lorraine. Presently he is an Emeritus Professor at the “Ecole Nationale Supérieure de Géologie”. Jose Ragot is researcher in the “Centre de Recherche en Automatique de Nancy” (CRAN, CNRS UMR 7039) where he was the head of the group “process diagnosis” during 12 years. His major research fields include data validation and reconciliation, process diagnosis, fault detection and isolation. A part of his activities is devoted to identification and state estimation, adapted to process diagnosis and mainly in the field of multimodels. A list of these publications can be founded at http://perso.ensem.inpl-nancy.fr/Jose.Ragot/.

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Benaicha, A., Mourot, G., Benothman, K. et al. Determination of principal component analysis models for sensor fault detection and isolation. Int. J. Control Autom. Syst. 11, 296–305 (2013). https://doi.org/10.1007/s12555-012-0142-x

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  • DOI: https://doi.org/10.1007/s12555-012-0142-x

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