Document IIF
Réseaux bayésiens pilotés par la fusion des données et des connaissances pour un diagnostic interprétable des défaillances des systèmes CVC.
Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems.
Résumé
Timely diagnosis and maintenance of faults in chiller units is beneficial for reducing energy consumption in buildings. In order to improve the diagnostic accuracy and provide reasonable explanations for fault occurrences of the heating, ventilation, and air conditioning (HVAC) systems in buildings, a fusion-driven Bayesian network fault diagnosis method is proposed based on broad learning system and fuzzy expert knowledge. Firstly, from the data-driven perspective, an efficient broad learning system is developed as benchmarking model to build the prior Bayesian network structure. Secondly, from the knowledge-driven perspective, the fuzzy logic system is used to fuzz expert knowledge, which then used to optimize the Bayesian network. Finally, the information obtained by experts on-site is incorporated into the optimized Bayesian network as new evidence nodes to determine the Bayesian network. The parameters of the Bayesian network are learned through Noisy-MAX processing of the conditional probability table. The performance of the proposed method is evaluated based on the fault diagnosis of a chiller system. The results demonstrate that the Bayesian network established through this method combines the advantages of data-driven and knowledge-driven approaches. It not only performs well in terms of diagnostic accuracy, with accuracy rates above 98 % for six typical faults, but also provides effective explanations for the underlying mechanisms of the faults through causal relationship diagrams.
Documents disponibles
Format PDF
Pages : 101-112
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 : Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems.
- Identifiant de la fiche : 30032313
- Langues : Anglais
- Sujet : Technologie
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 161
- Date d'édition : 05/2024
- DOI : http://dx.doi.org/10.1016/j.ijrefrig.2024.02.019
Liens
Voir d'autres articles du même numéro (20)
Voir la source
-
Model-free HVAC control in buildings: a review.
- Auteurs : MICHAILIDIS P., MICHAILIDIS I., VAMVAKAS D., KOSMATOPOULOS E.
- Date : 10/2023
- Langues : Anglais
- Source : Energies - vol. 16 - n. 20
- Formats : PDF
Voir la fiche
-
Research on fault diagnosis strategy of air-con...
- Auteurs : MA Q., YUE C., YU M., SONG Y., CUI P., YU Y.
- Date : 02/2024
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 158
- Formats : PDF
Voir la fiche
-
A comprehensive review: Fault detection, diagno...
- Auteurs : SINGH V., MATHUR J., BHATIA A.
- Date : 12/2022
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 144
- Formats : PDF
Voir la fiche
-
IoT intelligent agent based cloud management sy...
- Auteurs : DU Z., CHEN S., ANDUV B., ZHU X., JIN X.
- Date : 02/2023
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 146
- Formats : PDF
Voir la fiche
-
Exploration of intelligent HVAC operation strat...
- Auteurs : LIU X.
- Date : 2020
- Langues : Anglais
- Formats : PDF
Voir la fiche