Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine
Widodo, Achmad, Yang, Bo-Suk, Kim, Eric Y. H., Tan, Andy C.C., & Mathew, Joseph (2009) Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine. Nondestructive Testing and Evaluation, 24(4), pp. 313-328.
This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.
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|Item Type:||Journal Article|
|Keywords:||fault diagnosis, low speed bearings, acoustic emission, relevance vector machine, support vector machine, component analysis|
|ISSN:||1477-2671 (Ionline) 1058-9759 (print)|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Civil Engineering & Built Environment
Current > Institutes > Institute of Health and Biomedical Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2009 Taylor & Francis|
|Copyright Statement:||Author's Pre-print: author can archive pre-print (ie pre-refereeing)
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Publisher's Version/PDF: author cannot archive publisher's version/PDF
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|Deposited On:||29 Apr 2014 23:59|
|Last Modified:||02 Jun 2014 02:47|
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