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A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition
Abdelkabir Bacha, Jamal Benhra and Ahmed Haroun Sabry. A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition. Communications on Applied Electronics 5(8):41-48, August 2016. BibTeX
@article{10.5120/cae2016652353, author = {Abdelkabir Bacha and Jamal Benhra and Ahmed Haroun Sabry}, title = {A CNC Machine Fault Diagnosis Methodology based on Bayesian Networks and Data Acquisition}, journal = {Communications on Applied Electronics}, issue_date = {August 2016}, volume = {5}, number = {8}, month = {Aug}, year = {2016}, issn = {2394-4714}, pages = {41-48}, numpages = {8}, url = {http://www.caeaccess.org/archives/volume5/number8/643-2016652353}, doi = {10.5120/cae2016652353}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, 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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Keywords
Fault diagnosis, Fault detection, Bayesian Networks, CNC machine, Flexible manufacturing, Data acquisition.