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Stratégies de diagnostic des défaillances et des erreurs de captation pour un système frigorifique à compression de vapeur utilisant des stratégies d'inférence floues et un réseau neuronal artificiel.

Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network.

Auteurs : KOCYIGIT N.

Type d'article : Article, Article de la RIF

Résumé

A fuzzy inference system (FIS) and an artificial neural network (ANN) were used for diagnosis of the faults of a vapor compression refrigeration experimental setup. A separate FIS was developed for detection of sensor errors. The fault estimation error of the FIS and ANN were evaluated by using the experimentally obtained sensor data. Separate FIS estimated the system faults and detected defective sensors in all test cases without any error. Levenberg Marquart (LM) type ANN algorithm was implemented to diagnose the system faults. Scaled conjugate gradient (SCG) and resilient backpropagation (RB) network type were also used to compare performances with the estimation of the LM algorithm. The LM type ANN estimated all fault conditions accurately in the test cases never observed before. The study demonstrated that the FIS and ANN could be used effectively to estimate the faulty conditions of the vapor compression refrigeration system.

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Pages : 69-79

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Détails

  • Titre original : Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network.
  • Identifiant de la fiche : 30013110
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 50
  • Date d'édition : 02/2015

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