Probabilistic Reasoning for Improving the Predictive Maintenance of Vital Electrical Machine: Case Study

Abstract

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.

 

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.