Fault diagnostics of industrial robots using support vector machines and discrete wavelet transforms

Datta, A; Patel, S; Mavroidis, C; Antoniadis, I; Krishnasamy, J; Hosek, M

dc.contributor.author	Datta, A	en
dc.contributor.author	Patel, S	en
dc.contributor.author	Mavroidis, C	en
dc.contributor.author	Antoniadis, I	en
dc.contributor.author	Krishnasamy, J	en
dc.contributor.author	Hosek, M	en
dc.date.accessioned	2014-03-01T02:50:23Z
dc.date.available	2014-03-01T02:50:23Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35098
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-80054876623&partnerID=40&md5=c170b5dce5552f7d45d09e279056e541	en
dc.subject.other	Operating conditions	en
dc.subject.other	Robotic systems	en
dc.subject.other	Semiconductor industry	en
dc.subject.other	Discrete wavelet transforms	en
dc.subject.other	Failure analysis	en
dc.subject.other	Feature extraction	en
dc.subject.other	Manipulators	en
dc.subject.other	Sensors	en
dc.subject.other	Support vector machines	en
dc.subject.other	Industrial robots	en
dc.title	Fault diagnostics of industrial robots using support vector machines and discrete wavelet transforms	en
heal.type	conferenceItem	en
heal.publicationDate	2006	en
heal.abstract	In this paper we address the problem of fault diagnostics in industrial robots. The goal was to develop a method that automatically, accurately and in a generic way could identify and classify faults once they occur for any type of industrial robot used. Although a large number of diagnosis methods and relevant applications for industrial equipment already exist, the current research in the area of fault diagnosis of industrial robotic manipulators is rather poor, due to the large variability of faults, the unsteady and non-uniform operating conditions, the small amount of sensors used in industrial manipulators and the rather limited time records of the equipment. These restrictions present key challenges of the current research to be undertaken. In this paper we present a novel approach to perform fault diagnostics of industrial robotic systems using Support Vector Machines (SVM) and Discrete Wavelet Transform based feature extraction. Experimental results are obtained from an industrial manipulator used in the semi-conductor industry. Copyright © 2006 by ASME.	en
heal.journalName	American Society of Mechanical Engineers, Manufacturing Engineering Division, MED	en