The　open-circuit　faults　of　EV　charging　piles　affect　power　quality　of　power　grid　and　threaten　charging　safety.　Researches　on　the　open-circuit　fault　diagnosis　are　of　great　significance　to　ensuring　the　safe　and　stable　of　the　power　grid　and　reducing　the　maintenance　cost　of　the　charging　pile.　According　to　the　multidimensional　characteristics　of　open　circuit　fault　signals　of　charging　piles,　a　tensor　reshape　fusion　diagnostic　model　is　proposed　in　this　paper.　In　this　method,　the　multidimensional　feature　parallel　extraction　ability　of　residual　network　(ResNet)　and　the　sequential　feature　extraction　ability　of　gated　recurrent　unit　(GRU)　are　used　to　extract　the　front　and　rear　stage　fault　features　of　the　charging　pile　circuit,　the　fusion　diagnosis　is　performed　for　front　and　rear　stage　features,　and　the　high　accuracy　fault　diagnosis　of　front　and　rear　stage　of　charging　pile　is　realized.　A　pre-processing　method　of　front-stage　three-phase　fault　signals　based　on　tensor　reshape　method　is　proposed,　which　avoids　the　graphical　input　or　one-dimensional　input　used　by　traditional　deep　learning　algorithms　and　gives　full　play　to　the　diagnostic　performance　of　deep　neural　network.　Compared　with　the　traditional　fault　diagnosis　method,　the　proposed　method　uses　deep　learning　technology,　which　does　not　require　manual　selection　of　fault　characteristic　parameters.　The　simulation　experiments　show　that　the　average　accuracy　of　the　proposed　method　can　reach　above　96%　under　the　influence　of　different　intensity　noise;　especially　in　the　case　of　high　noise　with　signal-noise　ratio　(SNR)　of　10dB,　the　accuracy　is　still　above　90%,　and　the　accuracy　of　this　method　reaches　94.54%　in　experimental,　which　further　verifies　the　effectiveness　of　this　method.　©　2023　Chin.Soc.for　Elec.Eng.