Determination of Principal Component Analysis models for sensor fault detection and isolation

Anissa Benaicha (1) , Gilles Mourot (2) , Kamel Benothman (3) , José Ragot (2)

1 Laboratoire Automatique et Traitement de Signal et Image [Monastir]
2 CRAN - Centre de Recherche en Automatique de Nancy
3 LR-Automatique-ENIT - Laboratoire d'Automatique [Tunis]

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.

Mots clés: variable reconstruction; PCA; sensor fault; fault detection and isolation; number of principal components; variable reconstruction.

Domaines: Automatique

Anissa Benaicha, Gilles Mourot, Kamel Benothman, José Ragot. Determination of Principal Component Analysis models for sensor fault detection and isolation. International Journal of Control, Automation and Systems, 2013, 11 (2), pp.296-305. ⟨10.1007/s12555-012-0142-x⟩. ⟨hal-00722762⟩