Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

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dc.authoridUyaroglu, Yilmaz/0000-0001-5897-6274
dc.contributor.authorSimsir, Mehmet
dc.contributor.authorBayjr, Raif
dc.contributor.authorUyaroglu, Yilmaz
dc.date.accessioned2024-09-29T16:04:52Z
dc.date.available2024-09-29T16:04:52Z
dc.date.issued2016
dc.departmentKarabük Üniversitesien_US
dc.description.abstractLow power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hubmotor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.en_US
dc.identifier.doi10.1155/2016/7129376
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid26819590en_US
dc.identifier.scopus2-s2.0-84956874945en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1155/2016/7129376
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6374
dc.identifier.volume2016en_US
dc.identifier.wosWOS:000369240300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStatoren_US
dc.subjectAlgorithmen_US
dc.subjectDesignen_US
dc.titleReal-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Networken_US
dc.typeArticleen_US

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