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Document IIF

Détection de panne et diagnostic d'un système frigorifique par utilisation d'un réseau neuronal probabiliste.

Fault detection and diagnosis of a refrigeration system using probabilistic neural network.

Numéro : pap. n. 759

Auteurs : LIANG Q., HAN H., CUI X., et al.

Résumé

Aiming at finding an efficient way for the fault detection and diagnosis (FDD) of refrigeration system, the probabilistic neural network (PNN) is proposed to diagnose 7 types of typical faults for a refrigeration system. The establishment of the FDD model based on PNN and the processes of finding out the best spread value was elaborated in detail. The influence of sample size on the best spread value and the correct rate (CR) were explored. It was also demonstrated that the system-level faults were more difficult to be recognized by the model than the component-level faults. The comparison also has been done between the performance of the PNN and the prevailing back-propagation (BP) network. The results show that the overall diagnosis performance of the PNN is better than that of the BP network and the diagnosis of single training of the PNN is more reliable.

Documents disponibles

Format PDF

Pages : 8 p.

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Fault detection and diagnosis of a refrigeration system using probabilistic neural network.
  • Identifiant de la fiche : 30015483
  • Langues : Anglais
  • Source : Proceedings of the 24th IIR International Congress of Refrigeration: Yokohama, Japan, August 16-22, 2015.
  • Date d'édition : 16/08/2015
  • DOI : http://dx.doi.org/10.18462/iir.icr.2015.0759

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