Sufficient　labeled　fault　samples　are　the　key　to　ensuring　the　performance　of　deep　learning　diagnostic　models.　However,　in　practical　engineering　applications,　machinery　and　equipment　operate　normally　most　of　the　time,　and　it　is　difficult　to　collect　enough　fault　data.　In　contrast,　through　the　kinetics　analysis　method,　simulation　data　sets　of　various　fault　types　can　be　easily　obtained.　However,　there　is　a　difference　in　distribution　between　simulation　data　and　real　data.　The　deep　learning　diagnosis　model　trained　directly　with　simulation　data　lacks　versatility　and　cannot　be　applied　to　fault　diagnosis　of　real　data.　To　this　end,　this　paper　proposes　a　simulation　data-driven　deep　transfer　learning　fault　diagnosis　method,　which　applies　the　fault　diagnosis　knowledge　in　the　simulation　data　to　the　real　data　fault　diagnosis　task.　The　effectiveness　of　the　method　is　verified　by　experiments　on　the　two-stage　planetary　gearbox　in　the　Drivetrain　Diagnostics　Simulator　(DDS)　test　bench.　Copyright　©　2022　Zicheng　Xiong,　et　al.