title: Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network.
abstract: Low 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 hub motor 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.
authors.affiliation: Sakarya University Institute of Natural Sciences, 54187 Sakarya, Turkey.
authors.forename: Mehmet
authors.initials: M
authors.lastname: Şimşir
authors.affiliation: Karabük University Technology Faculty, 78050 Karabük, Turkey.
authors.forename: Raif
authors.initials: R
authors.lastname: Bayır
authors.affiliation: Sakarya University Engineering Faculty, 54187 Sakarya, Turkey.
authors.forename: Yılmaz
authors.initials: Y
authors.lastname: Uyaroğlu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D011211:Electric Power Supplies; D004581:Electronics; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer
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