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.　©2025　Chin.Soc.for　Elec.Eng.