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dc.contributor.authorPurarjomaldlangrudi, Afrooz
dc.contributor.authorGhapanchi, Amir Hossein
dc.contributor.authorEsmalifalak, Mohammad
dc.date.accessioned2017-05-03T16:08:22Z
dc.date.available2017-05-03T16:08:22Z
dc.date.issued2014
dc.identifier.issn0263-2241
dc.identifier.doi10.1016/j.measurement.2014.05.029
dc.identifier.urihttp://hdl.handle.net/10072/65058
dc.description.abstractRolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom343
dc.relation.ispartofpageto352
dc.relation.ispartofjournalMeasurement
dc.relation.ispartofvolume55
dc.rights.retentionY
dc.subject.fieldofresearchInformation Systems Management
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchMechanical Engineering
dc.subject.fieldofresearchcode080609
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0913
dc.titleA Data Mining Approach for Fault Diagnosis: An Application of Anomaly Detection Algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorGhapanchi, Amir Hossein H.


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