The twenty-first century is witnessing the growth of electric vehicles due to the declining level of petroleum products and legal concern for clean technology to take care of environmental pollution. Battery and electric motor are the two important components in the electric vehicle. An electric motor is a prime component responsible for the propulsion of a vehicle. Because of the continuous operation and load variation, the motor is subjected to different types of faults. Thus, condition monitoring and on-board diagnosis of an electric motor in the electric vehicle is essential to avoid catastrophic failure. In India, it is observed that the induction motor is commonly used in electric vehicles for propulsion. This article proposes the methodology for condition monitoring and fault identification of components in the induction motor using an on-board diagnostics in an electric vehicle.
Induction motors are prime component in the industries. Hence, condition monitoring and fault diagnosis of induction motor are important to avoid shutdowns and unplanned maintenance. A technique based on time-domain grayscale current signal imaging (TDGCI) and convolutional neural network (CNN) is proposed for intelligent fault detection of broken rotor bar in an induction motor. The standard current signal dataset made available by the Aline Elly Treml Western Parana State University is used for analysis. This dataset is acquired by simulating the healthy and broken rotor bar (BRB) fault conditions with the four increasing severity levels (1BRB, 2BRB, 3BRB, and 4BRB) at eight loading conditions varying from no load to full load. Conventional machine learning techniques have the limitations of feature selection, while the proposed technique can automatically extract the features from the given input image. The TDGCIs obtained from the time-domain current signal is used as input to exploit the enormous capability of CNN to carry out the image classification, thereby classifying faults features embedded in the images. The efforts are presented to design CNN parameters to achieve the fault classification accuracy of 99.58% for all cases with optimized computational time. The significant reduction in the computational time for fault classification compared to the peer published work is an important contribution.
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