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.
Highlights
The increasing needs of industry and daily life make electrical energy and ergo electrical motors more and more important
A fault diagnosis system was presented by Bayır and Bay for a serial wound starter motor based on a multilayer feedforward artificial neural network [12]
This study presents the designing of the artificial feedforward backpropagation neural network based real-time monitoring and fault diagnosis system and manufacturing of an applicable prototype of the system which was embedded into the microcontroller circuit for mobile applications
Summary
The increasing needs of industry and daily life make electrical energy and ergo electrical motors more and more important. Various studies were presented about condition monitoring, fault diagnosis, and detection using intelligent systems and mostly neural networks over the years. A fault diagnosis system was presented by Bayır and Bay for a serial wound starter motor based on a multilayer feedforward artificial neural network [12]. This study presents the designing of the artificial feedforward backpropagation neural network based real-time monitoring and fault diagnosis system and manufacturing of an applicable prototype of the system which was embedded into the microcontroller circuit for mobile applications. For this purpose, a test set for a hub motor was designed.
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