Publication:
Vibration Signal for Bearing Fault Detection using Random Forest

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dc.citedby5
dc.contributor.authorAbedin T.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorPhing C.C.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorTan J.D.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorKadirgama K.en_US
dc.contributor.authorBenedict F.en_US
dc.contributor.authorid57226667845en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid57884999200en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid36130958600en_US
dc.contributor.authorid12761486500en_US
dc.contributor.authorid57194591957en_US
dc.date.accessioned2024-10-14T03:20:50Z
dc.date.available2024-10-14T03:20:50Z
dc.date.issued2023
dc.description.abstractBased on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12017
dc.identifier.doi10.1088/1742-6596/2467/1/012017
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85160519158
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85160519158&doi=10.1088%2f1742-6596%2f2467%2f1%2f012017&partnerID=40&md5=0702bee41810d14a0328d1e0d220b38b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34581
dc.identifier.volume2467
dc.publisherInstitute of Physicsen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Physics: Conference Series
dc.subjectBearing
dc.subjectFault Detection
dc.subjectPrincipal Component Analysis
dc.subjectRandom Forest
dc.subjectClassification (of information)
dc.subjectFault detection
dc.subjectForestry
dc.subjectInduction motors
dc.subjectLearning systems
dc.subjectVibration analysis
dc.subjectBearing
dc.subjectBearing fault detection
dc.subjectData frames
dc.subjectFaults detection
dc.subjectOuter races
dc.subjectPrincipal-component analysis
dc.subjectProperty
dc.subjectRandom forest classifier
dc.subjectRandom forests
dc.subjectVibration signal
dc.subjectPrincipal component analysis
dc.titleVibration Signal for Bearing Fault Detection using Random Foresten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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