Dual-active-bridge　(DAB)　converter　can　connect　several　ac　distribution　networks　with　different　voltage　levels　by　a　common　dc　bus　to　form　ac/dc　hybrid　distribution　networks.　Its　reliability　issue　has　become　a　hot　research　topic　in　academia　and　industry.　In　this　article,　the　fault　operation　mode　of　DAB　is　analyzed　in　detail,　and　a　fault　diagnosis　strategy　based　on　adaptive　threshold　denoising　for　residual　networks　is　proposed　on　this　basis.　The　main　module　of　the　diagnosis　algorithm　consists　of　an　improved　channel　attention　module　and　threshold　algorithm.　The　improved　channel　attention　module　improves　the　network＇s　ability　to　learn　features　by　using　two　ways　to　extract　different　features　of　the　fault　signal　separately.　The　threshold　algorithm　is　embedded　into　the　deep　network　as　a　nonlinear　layer.　The　thresholds　can　be　learned　adaptively　in　the　established　cells,　which　plays　a　good　adaptive　denoising　capability.　Finally,　by　building　a　hardware-in-the-loop　system,　we　compare　the　signal　denoising　ability　of　various　methods　at　different　signal-to-noise　ratios.　The　experimental　results　demonstrate　that　the　proposed　method　can　accurately　locate　the　open-circuit　fault　and　achieve　99.82%　accuracy　under　the　original　data.　It　is　also　more　accurate　than　other　methods　under　different　signal-to-noise　ratios,　which　verifies　the　correctness　and　effectiveness　of　the　method.