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Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling / Hyun Cheol Cho in IEEE Transactions on control systems technology, Vol. 18 N° 2 (Mars 2010) 

Public ISBD [article]  in IEEE Transactions on control systems technology > Vol. 18 N° 2 (Mars 2010) . - pp. 430-437 Titre : Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling Type de document : texte imprimé Auteurs : Hyun Cheol Cho, Auteur ; Jeremy Knowles, Auteur ; M. Sami Fadali, Auteur Année de publication : 2011 Article en page(s) : pp. 430-437 Note générale : Génie Aérospatial Langues : Anglais (eng) Index. décimale : 629.1 Résumé : Dynamic neural models provide an attractive means of fault detection and isolation in industrial process. One approach is to create a neural model to emulate normal system behavior and additional models to emulate various fault conditions. The neural models are then placed in parallel with the system to be monitored, and fault detection is achieved by comparing the outputs of the neural models with the real system outputs. Neural network training is achieved using simultaneous perturbation stochastic approximation (SPSA). Fault classification is based on a simple threshold test of the residuals formed by subtracting each neural model output from the corresponding output of the real system. We present a new approach based on this well known scheme where a Bayesian network is used to evaluate the residuals. The approach is applied to fault detection in a three-phase induction motor. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5152950 [article] Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling [texte imprimé] / Hyun Cheol Cho, Auteur ; Jeremy Knowles, Auteur ; M. Sami Fadali, Auteur . - 2011 . - pp. 430-437. Génie Aérospatial Langues : Anglais (eng) in IEEE Transactions on control systems technology > Vol. 18 N° 2 (Mars 2010) . - pp. 430-437 Index. décimale : 629.1 Résumé : Dynamic neural models provide an attractive means of fault detection and isolation in industrial process. One approach is to create a neural model to emulate normal system behavior and additional models to emulate various fault conditions. The neural models are then placed in parallel with the system to be monitored, and fault detection is achieved by comparing the outputs of the neural models with the real system outputs. Neural network training is achieved using simultaneous perturbation stochastic approximation (SPSA). Fault classification is based on a simple threshold test of the residuals formed by subtracting each neural model output from the corresponding output of the real system. We present a new approach based on this well known scheme where a Bayesian network is used to evaluate the residuals. The approach is applied to fault detection in a three-phase induction motor. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5152950