Fault classification of low-speed bearings based on support vector machine for regression and genetic algorithms using acoustic emission

dc.contributor.authorOmoregbee, Henry Ogbemudia
dc.contributor.authorHeyns, P.S. (Philippus Stephanus)
dc.contributor.emailstephan.heyns@up.ac.zaen_ZA
dc.date.accessioned2019-12-05T08:26:36Z
dc.date.issued2019-10
dc.description.abstractPURPOSE : This work under consideration makes use of support vector machines (SVM) for regression and genetic algorithms (GA) which may be referred to as SVMGA, to classify faults in low-speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions. METHODS : GA is used as a heuristic tool in finding profound solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and hence without the forehand knowledge of the probabilistic distribution can form new features from the original dataset. Using SVM for regression, the non-linear regression and fault recognition are achieved. Classification is performed for three classes. In this work, the GA is used to first optimize the statistical features for the best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speed, regardless of the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions. RESULTS : This study accomplished the effective classification of different bearing fault patterns especially at low speeds and at varying load conditions using support vector machines (SVM) for regression and genetic algorithms (GA) referred to as SVMGA.en_ZA
dc.description.departmentMechanical and Aeronautical Engineeringen_ZA
dc.description.embargo2020-06-12
dc.description.librarianhj2019en_ZA
dc.description.urihttps://www.springer.com/journal/42417en_ZA
dc.identifier.citationOmoregbee, H.O. & Heyns, P.S. Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission. Journal of Vibration Engineering & Technologies (2019) 7: 455-464. https://doi.org/10.1007/s42417-019-00143-y.en_ZA
dc.identifier.issn2523-3920 (print)
dc.identifier.issn2523-3939 (online)
dc.identifier.other10.1007/s42417-019-00143-y
dc.identifier.urihttp://hdl.handle.net/2263/72527
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© Krishtel eMaging Solutions Private Limited 2019. The original publication is available at : https://www.springer.com/journal/42417.en_ZA
dc.subjectAcoustic emission (AE)en_ZA
dc.subjectArtificial intelligence (AI)en_ZA
dc.subjectArtificial neural networks (ANN)en_ZA
dc.subjectExponential kernel functionen_ZA
dc.subjectGaussian kernel functionen_ZA
dc.subjectRolling element bearingen_ZA
dc.subjectSupport vector machines (SVM)en_ZA
dc.subjectGenetic algorithm (GA)en_ZA
dc.subjectDiagnosisen_ZA
dc.subject.otherEngineering, built environment and information technology articles SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology articles SDG-12
dc.subject.otherSDG-12: Responsible consumption and production
dc.subject.otherEngineering, built environment and information technology articles SDG-08
dc.subject.otherSDG-08: Decent work and economic growth
dc.subject.otherEngineering, built environment and information technology articles SDG-04
dc.subject.otherSDG-04: Quality education
dc.titleFault classification of low-speed bearings based on support vector machine for regression and genetic algorithms using acoustic emissionen_ZA
dc.typePostprint Articleen_ZA

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