Intelligent　fault　diagnosis　of　machines　has　long　been　a　research　hotspot　and　has　achieved　fruitful　results.　However,　intelligent　fault　diagnosis　is　a　difficult　issue　in　the　case　of　a　small　sample　due　to　the　lack　of　fault　signals.　To　solve　this　problem,　a　small　sample　focused　intelligent　fault　diagnosis　method　via　multimodules　gradient　penalized　generative　adversarial　networks　is　proposed.　The　proposed　method　consists　of　three　network　modules:　generator,　discriminator,　and　classifier.　By　adversarial　training,　the　generator　can　generate　mechanical　signals　in　different　health　conditions.　Because　of　the　high　similarity　to　the　signals　obtained　in　practice,　the　generated　signals　can　also　be　used　as　training　data　so　that　the　limited　training　dataset　of　the　proposed　method　is　expanded.　The　classifier　has　a　strong　ability　to　extract　fault　features　from　raw　mechanical　signals　and　then　classify　different　health　conditions.　The　experimental　results　on　two　bearing　vibration　datasets　indicate　that　the　proposed　method　can　not　only　generate　bearing　vibration　signals　but　also　obtain　fairly　high　fault　classificati　on　accuracy　under　the　small　sample　condition.