Résumé

This paper presents a model to predict Electrical Power Generator (EPG) faults. The fault tree (FT) model is developed and used to help maintenance engineers in fault analysis procedure of this rotating machine. By identifying the main, intermediate and basic events it’s possible to construct the FT with logical reasoning. The top dreaded event is defined. By using a Bayesian network (BN) as a complementary tool, fault prediction of the EPG becomes possible and easy. By using the developed BN, the probability of occurrence of the top event (EPG failure) is calculated. Also, by this
approach, we can process complex information that causes system faults in an easy and simple way. The essential elements to do this analysis are the reliable and good exploitation of the information previously stored in the system. The use of the BN in combination with the FT gives the possibility of qualitative and quantitative analysis, diagnosis, and prediction of faults from the same Bayesian model. The flexibility of the proposed BN model in this paper allows better and precise decision making. Also, priorities regarding maintenance job are defined and resources are a priori prepared.

Information

Item Type:	Journal
Divisions:	» Laboratoire de Recherche en Électromécanique et Sûreté de Fonctionnement » Faculté des Sciences et de la Technologie
ePrint ID:	4922
Date Deposited:	2024-03-19
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/fr/publication/article/4922

BibTex