Coupling Pattern Recognition With State Estimation Using Kalman Filter for Fault Diagnosis - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Industrial Electronics Année : 2012

Coupling Pattern Recognition With State Estimation Using Kalman Filter for Fault Diagnosis

Olivier Ondel
Emmanuel Boutleux
Eric Blanco
Guy Clerc

Résumé

This paper deals with a diagnosis tool based on a pattern recognition approach associated with Kalman interpolator/extrapolator. The first aim is to decrease the number of measurements to realize while increasing the learning database contents using a Kalman state estimator. The second one is to estimate, from the initial set of measured data, future states of the studied process. A 5.5-kW induction motor bench is used as an application to validate this approach. First, a signature is determined in order to monitor the different operating modes evolution. Diagnostic features are extracted only from current and voltage sensors. Then, a feature selection method is applied in order to select the most relevant features for diagnosis. Finally, a Kalman filter algorithm is developed in order to interpolate the known states and to predict evolution toward new ones. A new diagnosis tool is then designed handling continuous evolution (severity, load) inside the different operating modes (healthy, stator fault, ...).
Fichier non déposé

Dates et versions

hal-00730655 , version 1 (10-09-2012)

Identifiants

Citer

Olivier Ondel, Emmanuel Boutleux, Eric Blanco, Guy Clerc. Coupling Pattern Recognition With State Estimation Using Kalman Filter for Fault Diagnosis. IEEE Transactions on Industrial Electronics, 2012, 59 (11), pp.4293 - 4300. ⟨10.1109/TIE.2011.2181133⟩. ⟨hal-00730655⟩
105 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More