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Fault Detection of Bearing using Support Vector Machine-SVM

Bearings (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 machines

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Bibliographic Details
Main Authors: Borhana A.A., Bin Mustaffa Kamal D.D., Latif S.D., Ali Y.H., Ahmed Almahfoodh A.N., El-Shafie A.
Other Authors: 55212152300
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-253242023-05-29T16:08:10Z Fault Detection of Bearing using Support Vector Machine-SVM Borhana A.A. Bin Mustaffa Kamal D.D. Latif S.D. Ali Y.H. Ahmed Almahfoodh A.N. El-Shafie A. 55212152300 57220804431 57216081524 56225555300 57214837520 16068189400 Bearings (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 machines Modern 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. Final 2023-05-29T08:08:10Z 2023-05-29T08:08:10Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243507 2-s2.0-85097645813 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097645813&doi=10.1109%2fICIMU49871.2020.9243507&partnerID=40&md5=8fda654bf122c790e550832ef00df9b9 https://irepository.uniten.edu.my/handle/123456789/25324 9243507 309 315 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Bearings (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 machines
author2 55212152300
author_facet 55212152300
Borhana A.A.
Bin Mustaffa Kamal D.D.
Latif S.D.
Ali Y.H.
Ahmed Almahfoodh A.N.
El-Shafie A.
format Conference Paper
author Borhana A.A.
Bin Mustaffa Kamal D.D.
Latif S.D.
Ali Y.H.
Ahmed Almahfoodh A.N.
El-Shafie A.
spellingShingle Borhana A.A.
Bin Mustaffa Kamal D.D.
Latif S.D.
Ali Y.H.
Ahmed Almahfoodh A.N.
El-Shafie A.
Fault Detection of Bearing using Support Vector Machine-SVM
author_sort Borhana A.A.
title Fault Detection of Bearing using Support Vector Machine-SVM
title_short Fault Detection of Bearing using Support Vector Machine-SVM
title_full Fault Detection of Bearing using Support Vector Machine-SVM
title_fullStr Fault Detection of Bearing using Support Vector Machine-SVM
title_full_unstemmed Fault Detection of Bearing using Support Vector Machine-SVM
title_sort fault detection of bearing using support vector machine-svm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426358988931072
score 13.19449

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