关键词: 关键词：充电桩故障诊断, 图像分类, 视觉Transformer, 预训练技术

Abstract: Abstract: With the increasing penetration rate of electric vehicles and the increasing popularity of charging facilities， the difficulty of operation and maintaining electric vehicle charging facilities is also increasing. The fault diagnosis of Direct Current （DC） charging piles is a crucial part of the operation and maintenance of electric vehicle charging facilities. Detecting early faults of charging piles in time is of great significance in eliminating the risk of charging pile failures and ensuring the stable operation of charging piles. The existing fault diagnosis methods not only require the assistance of high-cost specialized equipment but also have high requirements for data sampling and feature extraction； Although the fault diagnosis method based on neural networks and its variants has high performance， its training process requires high-quality labelled data， and it is not easy to obtain enough labelled data for training. Therefore， a fault diagnosis method based on Vision Transformer （ViT） is proposed in this paper only by using the voltage and current signals collected by the DC charging pile itself. In this method， the low-frequency sampling signals of voltage and current of the charging pile are converted into time series images， and the ViT model is used for feature learning. In this process， the pre-training technology is used to transfer the cross-domain feature representation knowledge into the fault diagnosis model， so that the ViT model can be fine-tuned on a relatively small labelled data set， thus achieving better performance on limited data and alleviating the need for labelled data. The experimental results show that the average accuracy of the fault diagnosis model is 92.2%， which meets the practical requirements. The method proposed in this paper supports online diagnosis and does not depend on special equipment， so it has a good popularization prospects.

Key words: Key words: fault diagnosis for charging piles, image classification, Vision Transformer, pre-training techniques