A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

Abstract This paper introduces an advanced methodology employing Convolutional Neural Networks (CNNs) for fault detection in induction motors, with a special focus on electric vehicles (EVs). Induction motors are critical to the operational efficiency of EVs, where their performance directly affects vehicle safety, reliability, and range. Traditional fault detection methods often fail to keep pace with the demands of real-time diagnostics in the increasingly competitive EV market. To address this, this paper proposes a novel CNN-based fault detection system that leverages machine learning to perform non-invasive fault analysis through comprehensive feature extraction and classification from motor signal data. The model uses a combination of spatial and temporal data, processed through a hybrid architecture integrating CNNs with Temporal Convolutional Networks (TCNs) for enhanced fault identification accuracy. The testing and analysis of the model was performed on datasets generated from various EV models under different fault conditions, achieving an average accuracy of 92% in detecting and classifying motor faults, significantly outperforming traditional methods. The results highlight the effectiveness of the approach in early fault detection and its potential in reducing maintenance costs and downtime. This study not only contributes to the robust diagnostics of EV induction motors but also aligns with the broader objectives of Industry 4.0 by enhancing the integration of smart technologies in automotive diagnostics.