The week's pick
Reseach Article
An industrial Fault Diagnosis System based on Bayesian Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 124 - Number 5 |
| Year of Publication: 2015 |
| Authors: Abdelkabir Bacha, Ahmed Haroun Sabry, Jamal Benhra |
10.5120/ijca2015905484
|
Abdelkabir Bacha, Ahmed Haroun Sabry, Jamal Benhra . An industrial Fault Diagnosis System based on Bayesian Networks. International Journal of Computer Applications. 124, 5 ( August 2015), 1-7. DOI=10.5120/ijca2015905484
Abstract
This paper presents a DC motor fault diagnosis system based on Bayesian networks. This was done by the design of a new electromechanical test bed allowing the collection of functioning data from a real world industrial Direct current (DC) Motor. The data collection will help in the construction of Bayesian networks models. These data are collected from sensors measuring different types of variables that are directly related to the industrial system. Without doing any mathematical modeling that describes the physical properties of the studied DC motor, the proposed tool provides with the help of Bayesian networks parameters and structure learning algorithms, the base to construct a fault diagnosis tool that can be extended to a fault prognosis tool.
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