中国电机工程学报2025，Vol.45Issue(8)：2992-3002,中插12,12.DOI:10.13334/j.0258-8013.pcsee.232309
基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断
Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism
摘要
Abstract
With the growing adoption of electric vehicles,demand for charging infrastructure has increased significantly,highlighting the need for timely maintenance and fault diagnosis of charging piles.To effectively leverage multi-scale features in charging pile fault signals,this paper proposes a fault information fusion diagnosis method for vehicle-to-grid(V2G)charging piles with open-circuit switching tubes,based on a multi-scale convolutional neural network(CNN)and dual-attention mechanism.The approach builds upon CNNs by integrating a self-attention mechanism to emphasize critical fault signal features.Simultaneously,max pooling and average pooling layers process fault signals to extract complementary multi-scale information.Additionally,a channel attention mechanism is incorporated to enhance model performance by weighting different channel features.Fault classification is performed using a Softmax classifier.Simulation results demonstrate the method's superiority over other algorithms in convergence speed,overfitting suppression,and diagnostic accuracy,while exhibiting strong noise robustness—effectively handling noise interference in fault signals.Experimental tests show the method achieves 96.67%accuracy in locating open-circuit faults in switching tubes,providing an effective solution for diagnosing such faults in charging piles.关键词
充电桩/故障诊断/信息融合/深度学习/注意力机制Key words
charging pile/fault diagnosis/information fusion/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
徐玉珍,邹中华,刘宇龙,曾梓洋,文云,金涛..基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断[J].中国电机工程学报,2025,45(8):2992-3002,中插12,12.基金项目
国家自然科学基金项目(51977039,52377088).Project Supported by National Natural Science Foundation of China(51977039,52377088). （51977039,52377088）
