In　gas　turbine　rotor　systems,　an　intelligent　data-driven　fault　diagnosis　method　is　an　important　means　to　monitor　the　health　status　of　the　gas　turbine,　and　it　is　necessary　to　obtain　sufficient　fault　data　to　train　the　intelligent　diagnosis　model.　In　the　actual　operation　of　a　gas　turbine,　the　collected　gas　turbine　fault　data　are　limited,　and　the　small　and　imbalanced　fault　samples　seriously　affect　the　accuracy　of　the　fault　diagnosis　method.　Focusing　on　the　imbalance　of　gas　turbine　fault　data,　an　Improved　Deep　Convolutional　Generative　Adversarial　Network　(Improved　DCGAN)　suitable　for　gas　turbine　signals　is　proposed　here,　and　a　structural　optimization　of　the　generator　and　a　gradient　penalty　improvement　in　the　loss　function　are　introduced　to　generate　effective　fault　data　and　improve　the　classification　accuracy.　The　experimental　results　of　the　gas　turbine　test　bench　demonstrate　that　the　proposed　method　can　generate　effective　fault　samples　as　a　supplementary　set　of　fault　samples　to　balance　the　dataset,　effectively　improve　the　fault　classification　and　diagnosis　performance　of　gas　turbine　rotors　in　the　case　of　small　samples,　and　provide　an　effective　method　for　gas　turbine　fault　diagnosis.