For　the　problems　of　modeling　difficulties　and　signal　feature　extraction　in　inverter　open-circuit　fault　diagnosis　methods,　the　methods　based　on　machine　learning　and　deep　learning　still　have　limitations　in　practical　applications.　In　this　paper,　a　three-phase　inverter　fault　diagnosis　method　based　on　Markov　variational　field　and　residual　convolutional　network　is　proposed.　The　method　encodes　the　phase　voltage　signal　output　from　the　inverter　by　MTF　to　generate　a　two-dimensional　image　with　timing　information　and　state　migration　information,　avoiding　the　problem　of　signal　information　loss　when　directly　converting　voltage　or　circuit　signals　into　one-dimensional　or　two-dimensional　images.　At　the　same　time,　the　method　uses　ResNet　to　process　MTF　images　to　extract　fault　features　of　phase　voltage　signals　and　enhance　the　propagation　of　feature　information　so　that　the　feature　information　can　be　fully　utilized　and　accurate　fault　classification　and　identification　can　be　achieved.　The　experimental　results　show　that　the　proposed　method　can　effectively　identify　22　open-circuit　fault　types　of　three-phase　inverters　with　a　diagnostic　accuracy　of　98.10%.　©　2023　SPIE.