Detection of visual bearing defect using integrated artificial neural network

Agustian K. Herdianta*, Aulia M.T. Nasution

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The characteristics of bearing vibration can be used to detect common bearing defect, like noise defect etc. This method unfortunately can not detect the visual defects on the inner and outer ring bearing surface. A pattern recognition is implemented in this paper to solve the problem. A backpropagation neural network architecture is used to recognize the visual defect pattern. This architecture is integrated in a digital image processing chain. Recognition rate of good bearing is obtained at 92.93 %, meanwhile for defected bearing is obtained at 75 % respectively. This rate shows integrated artificial neural network with digital image processing can be implemented to detect the presence of visual bearing defect.

Original languageEnglish
Title of host publicationICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings
Pages391-394
Number of pages4
Publication statusPublished - 2011
Event2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011 - Jakarta, Indonesia
Duration: 17 Dec 201118 Dec 2011

Publication series

NameICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

Conference2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011
Country/TerritoryIndonesia
CityJakarta
Period17/12/1118/12/11

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