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Fault detection and diagnosis using hidden markov disturbance models / Wee Chin Wong in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7901–7908 Titre : Fault detection and diagnosis using hidden markov disturbance models Type de document : texte imprimé Auteurs : Wee Chin Wong, Auteur ; Jay H. Lee, Auteur Année de publication : 2010 Article en page(s) : pp 7901–7908 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Detection  Diagnosis  Markov  disturbance  models. Résumé : Fault detection and diagnosis is critical for maintaining the health of process systems. Common fault signals include process and disturbance parameter changes, as well as sensor and actuator malfunctions typically manifested as persistent drifts or abrupt biases. These may be characterized by the existence of latent “fault” states. This work examines the effectiveness of a hidden Markov model framework for modeling such fault regimes. The proposed methodology may be interpreted as a generalization of the commonly employed mixture-of-Gaussians approach and is demonstrated through a shell-and-tube heat exchanger problem. Furthermore, the flexibility of the method is shown in the context of detecting valve stiction, a significant problem in the process industries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9020655 [article] Fault detection and diagnosis using hidden markov disturbance models [texte imprimé] / Wee Chin Wong, Auteur ; Jay H. Lee, Auteur . - 2010 . - pp 7901–7908. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7901–7908 Mots-clés : Detection  Diagnosis  Markov  disturbance  models. Résumé : Fault detection and diagnosis is critical for maintaining the health of process systems. Common fault signals include process and disturbance parameter changes, as well as sensor and actuator malfunctions typically manifested as persistent drifts or abrupt biases. These may be characterized by the existence of latent “fault” states. This work examines the effectiveness of a hidden Markov model framework for modeling such fault regimes. The proposed methodology may be interpreted as a generalization of the commonly employed mixture-of-Gaussians approach and is demonstrated through a shell-and-tube heat exchanger problem. Furthermore, the flexibility of the method is shown in the context of detecting valve stiction, a significant problem in the process industries. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9020655