Research Article
A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
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| Volume 5 - Issue 8 |
| Published: Aug 2016 |
| Authors: Abdelkabir Bacha, Jamal Benhra, Ahmed Haroun Sabry |
10.5120/cae2016652353
|
Abdelkabir Bacha, Jamal Benhra, Ahmed Haroun Sabry . A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition. Communications on Applied Electronics. 5, 8 (Aug 2016), 41-48. DOI=10.5120/cae2016652353
@article{ 10.5120/cae2016652353,
author = { Abdelkabir Bacha,Jamal Benhra,Ahmed Haroun Sabry },
title = { A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition },
journal = { Communications on Applied Electronics },
year = { 2016 },
volume = { 5 },
number = { 8 },
pages = { 41-48 },
doi = { 10.5120/cae2016652353 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Abdelkabir Bacha
%A Jamal Benhra
%A Ahmed Haroun Sabry
%T A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition%T
%J Communications on Applied Electronics
%V 5
%N 8
%P 41-48
%R 10.5120/cae2016652353
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this work, a Bayesian Networks based fault diagnosis system for industrial machines is proposed. For this purpose, an experimental setup of a CNC machine is given as a test rig. This fault diagnosis system is composed of three levels: The first level concerns a set of sensors that are connected directly to the machine’s main organs. The second level is a microcontroller based data acquisition interface that calibrates and transfers the measured data to the third level. The last level is a set of machine learning algorithms that are executed in a computer. These algorithms perform BN structure learning and exploit this structure for classifying the new arrival data from the CNC machine and determining if it presents a faulty or a normal situation.
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