Enhancing Fault Diagnosis Accuracy in Electric Motors: A Digital Twin Approach with Transformer Model

This paper explores the integration of Digital Twin (DT) technology and machine learning (ML) techniques for predictive maintenance in electric vehicles (EVs), particularly focusing on fault diagnosis in electric motors (EMs). DT technology, which creates a digital replica of a physical entity, combined with real-time sensor data analytics, offers significant potential for improving the reliability and operational efficiency of EMs under variable and intense conditions. By incorporating advanced ML models like Transformers, which are adept at capturing long-range dependencies, into the DT framework, the study addresses critical challenges in fault diagnosis under non-stationary conditions typical in EVs. The research introduces a novel approach that combines Transformers with convolutional layers to enhance the fault diagnosis capabilities by effectively capturing complex patterns in time-series data. Furthermore, incremental learning is proposed within the DT framework to adapt to evolving environments and maintain high diagnostic performance, aiming for domain invariance in fault diagnosis models. The paper evaluates various deep learning models, including Transformers, FCNs, CNNs, Inception, ResNet, and MLP, across metrics such as accuracy, training duration, and inference time, identifying CNNs and FCNs as optimal due to their balance between performance and processing time. This study not only demonstrates the feasibility of advanced ML techniques in enhancing DT-based fault diagnosis but also provides insights into selecting appropriate models for predictive maintenance applications in EVs, highlighting the potential for future research in adapting these technologies to different datasets and operational scenarios.