Domain　adaptation-based　transfer　learning　methods　have　been　widely　investigated　in　fault　diagnosis　of　rotating　machinery,　but　their　basic　convolution　or　recurrent　structure　is　subject　to　poor　global　feature　representation　ability,　which　hinders　the　learning　of　domain-irrelevant　modulation　information.　In　addition,　the　＂black　box＂　nature　of　deep　learning　models　limits　their　applications　in　high　risk-sensitive　scenarios.　In　this　paper,　an　interpretable　domain　adaptation　transformer　(IDAT)　is　proposed　for　the　transferable　fault　diagnosis　of　rotating　machinery.　First,　a　multi-layer　domain　adaptation　transformer　framework　is　proposed,　which　can　capture　the　global　information　that　is　crucial　for　learning　the　modulation　information　of　different　domains,　and　meanwhile　reduce　the　feature　distribution　discrepancy.　Second,　an　ensemble　attention　weight　is　applied　to　enable　the　transfer　learning　framework　to　be　interpretable,　which　is　implemented　by　averaging　the　integral　values　of　the　multi-head　attention　maps　along　the　key　direction.　In　addition,　the　raw　vibration　signals　are　embedded　as　the　input　of　the　model,　which　provides　an　end-to-end　fault　diagnosis.　The　proposed　IDAT　is　tested　by　various　cross-condition　and　cross-machine　bearing　fault　diagnosis　tasks,　and　results　confirm　the　advantages　of　the　method.