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Nowadays, new information technologies produce new methodological approaches attempting to extract not just valid and reliable information, but more generally a particular technical and professional expertise to support the decision making. A Bayesian network was developed for fault assessment of an electrical motor. By inference, this model made it possible to calculate the probability of rotor fault of the induction motor, while defining the weakest branch in the structure of the Bayesian network that leads to failure by determining the probabilities of intermediate events. The most likely faults were then defined and the information system consolidated, as well as the decision-making process. The article ends with an application that shows the methodology developed and gives some results illustrated by figures.

Item Type	Journal
Divisions	» Faculty of Science and Technology
ePrint ID	2454
Date Deposited	2020-12-11
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/2454

@article{uniusa2454,
    title={Probabilistic reasoning for improving the predictive maintenance of vital electrical machine: Case study},
    author={Abdelaziz LAKEHAL, Ahmed Ramdane and Fouad Tachi},
    journal={Journal of Advanced Engineering and Computation}
    year={2018},
    volume={2},
    number={1},
    pages={9-17},
    publisher={}
}