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A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 8 |
| Year of Publication: 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
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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