Résumé

This paper presents a model to predict electrical generator (EG) failure. The fault tree (FT) tool is used to model the causes of rotating machines failure. By analyzing the basis events it’s possible to predict the top and adverseevent, which helps us in predicting the type of event that causes system failure. Also, Bayesian Networks (BN) tool is used to learn more about the impact of each event on the system function by quantifythe probabilities of occurrence of the highest event (EG failure). However, this methodcan treated complex informationthat causes systems failure in an easy and simple way. The success of the proposed probabilistic approach is the reliable information’spreviously stored in the system and their good exploitation. By predicting potential system failures we contribut to the qualitative and quantitative diagnosis of the EG and consequently its possible to optmize the dependability of the system.

Information

Item Type:	Conference
Divisions:	» Laboratoire de Recherche en Électromécanique et Sûreté de Fonctionnement » Faculté des Sciences et de la Technologie
ePrint ID:	3758
Date Deposited:	2022-12-29
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/fr/publication/article/3758

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