Fault data enhancement and real-time diagnosis using optimized ViT ++ algorithm for electric drive system

With the rapid development of electric drive technology for new energy vehicles, fault data identification of key components of electric drives has become a crucial issue in improving the stability and safety of electric systems. However, traditional fault data recognition methods have many limitations in dealing with complex and variable operating fault situations. To address this problem, this paper proposes a deep learning model, Vision Transformer Plus (ViT++), based on the self-attention mechanism and combined with data enhancement strategies for fault identification of energy vehicle electric drive system. Accurately identifying fault types is achieved by transforming the electric drive system fault data into an image matrix and performing feature extraction and learning with the help of the ViT model. To validate the effectiveness of the proposed method, we conducted extensive cross-experiments using a large amount of actual electric drive key component fault data and applying a data enhancement strategy. The experimental results show that the fault data recognition method based on the ViT algorithm has higher accuracy and robustness than the traditional convolutional neural network (CNN)-based method. Therefore, the proposed method in this paper is conducive to improving the accuracy and efficiency of fault data identification for key components in electric vehicles, thus playing a critical role in analyzing electric drive system faults.