In　the　process　of　gas　turbine　rotor　fault　diagnosis　based　on　data-driven,　transfer　learning　is　an　effective　method　to　solve　the　lack　of　gas　turbines　labeled　data,　which　will　result　in　domain　shifts　due　to　the　data　distribution　difference　between　source　domain　data　and　target　domain　data　under　variable　working　condition.　A　gas　turbine　fault　diagnosis　method　based　on　Adversarial　Discriminative　Domain　Adaptation　Transfer　Learning　Network　(ADDATLN)　is　put　forward　to　reduce　domain　offsets　and　improve　the　gas　turbine　fault　diagnosis　accuracy.　In　the　proposed　method,　pre-trained　deep　Convolutional　Neural　Networks　(CNN)　models　in　the　source　domain　is　transferred　to　target　domain　data,　then　deep　adversarial　training　between　the　source　domain　and　target　domain　is　adopted　to　adaptively　optimize　the　model　parameters　of　the　target　domain　network,　with　the　purpose　of　reducing　domain　offsets　and　improving　gas　turbine　fault　classification　accuracy.　Field　test　experiment　results　on　gas　turbine　rotor　fault　diagnosis　under　different　working　conditions　show　that　the　average　accuracy　of　the　proposed　method　reaches　96.45%,　and　the　average　accuracy　of　fault　diagnosis　on　different　gas　turbines　with　the　same　type　achieved　95.13%.　The　field　test　results　confirm　that　the　method　effectively　reduces　the　domain　differences　caused　by　varying　working　conditions　and　different　gas　turbines,　and　improves　the　accuracy　of　gas　turbine　rotor　fault　diagnosis　under　variable　working　condition　and　for　different　gas　turbines　with　small　samples.