Publication:
Fault Detection of Bearing using Support Vector Machine-SVM

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dc.citedby3
dc.contributor.authorBorhana A.A.en_US
dc.contributor.authorBin Mustaffa Kamal D.D.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAli Y.H.en_US
dc.contributor.authorAhmed Almahfoodh A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid55212152300en_US
dc.contributor.authorid57220804431en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid56225555300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:08:10Z
dc.date.available2023-05-29T08:08:10Z
dc.date.issued2020
dc.descriptionBearings (machine parts); Fault detection; Bearing failures; Input variables; Reliable models; Rolling elements; Support vector machine models; Trial-and-error method; Vibration frequency; Vibration signal; Support vector machinesen_US
dc.description.abstractModern spinning machinery is a crucial component of rolling element. The principal aim of this project is to create a support vector machine model, which is one of the AI techniques to detect and diagnose bearing fault at early stage. The development of the model should be able to forecast the bearing fault diameters based on the collected input variables. In order to achieve this objective, a set of bearing raw vibration frequency signal is acquired. The raw vibration signals were extracted. The extracted features are used as the inputs containing different motor loads, different motor speeds and different locations. The support vector machine approach is being used to run the simulation. The selection of kernel functions and other parameters are very important in the development of a reliable model. Trial and error method are used to identify the best combination of parameters for SVM model by comparing the MSE and CC values. The best kernel functions and parameters are set and the model is ready to be used to run the real data since it can provide the best and most accurate precision in early detecting bearing failures. Recommendation was made to improve the architecture of SVM model. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243507
dc.identifier.doi10.1109/ICIMU49871.2020.9243507
dc.identifier.epage315
dc.identifier.scopus2-s2.0-85097645813
dc.identifier.spage309
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097645813&doi=10.1109%2fICIMU49871.2020.9243507&partnerID=40&md5=8fda654bf122c790e550832ef00df9b9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25324
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleFault Detection of Bearing using Support Vector Machine-SVMen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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