To　address　the　problem　of　insufficient　real-world　data　on　planetary　gearboxes,　which　makes　it　difficult　to　diagnose　faults　using　deep　learning　methods,　it　is　possible　to　obtain　sufficient　simulation　fault　data　through　dynamic　simulation　models　and　then　reduce　the　difference　between　simulation　data　and　real　data　using　transfer　learning　methods,　thereby　applying　diagnostic　knowledge　from　simulation　data　to　real　planetary　gearboxes.　However,　the　label　space　of　real　data　may　be　a　subset　of　the　label　space　of　simulation　data.　In　this　case,　existing　transfer　learning　methods　are　susceptible　to　interference　from　outlier　label　spaces　in　simulation　data,　resulting　in　mismatching.　To　address　this　issue,　this　paper　introduces　multiple　domain　classifiers　and　a　weighted　learning　scheme　on　the　basis　of　existing　domain　adversarial　transfer　learning　methods　to　evaluate　the　transferability　of　simulation　data　and　adaptively　measure　their　contribution　to　label　predictor　and　domain　classifiers,　filter　the　interference　of　unrelated　categories　of　simulation　data,　and　achieve　accurate　matching　of　real　data.　Finally,　partial　transfer　experiments　are　conducted　to　verify　the　effectiveness　of　the　proposed　method,　and　the　experimental　results　show　that　the　diagnostic　accuracy　of　this　method　is　higher　than　existing　transfer　learning　methods.