To　address　the　issue　of　not　having　enough　labeled　fault　data　for　planetary　gearboxes　in　actual　production,　this　research　develops　a　simulation　data-driven　deep　transfer　learning　fault　diagnosis　method　that　applies　fault　diagnosis　knowledge　from　a　dynamic　simulation　model　to　an　actual　planetary　gearbox.　Massive　amounts　of　different　fault　simulation　data　are　collected　by　creating　a　dynamic　simulation　model　of　a　planetary　gearbox.　A　fresh　deep　transfer　learning　network　model　is　built　by　fusing　one-dimensional　convolutional　neural　networks,　attention　mechanisms,　and　domain　adaptation　methods.　The　network　model　is　used　to　learn　domain　invariant　features　from　simulated　data,　thereby　enabling　fault　diagnosis　on　real　data.　The　fault　diagnosis　experiment　is　verified　by　using　the　Drivetrain　Diagnostics　Simulator　test　bench.　The　validity　of　the　proposed　means　is　evaluated　by　comparing　the　diagnostic　accuracy　of　various　means　on　various　diagnostic　tasks.