Here,　aiming　at　the　problem　of　weaker　applicability　of　traditional　unsupervised　domain　adaptation　methods　in　multi-working　condition　rolling　bearing　fault　diagnosis　scenarios,　a　multi-source　domain　adaptative　residual　network　(MDARN)　was　proposed.　By　aligning　relevant　subdomains　from　multiple　source　domains,　MDARN　could　improve　its　fault　diagnosis　performance　under　multiple　working　conditions.　Firstly,　ResNeXt　residual　network　was　used　to　fully　extract　transferable　features　from　source　domain　and　target　domain.　Then,　the　local　maximum　mean　difference　(LMMD)　criterion　was　introduced　to　align　relevant　subdomains　in　target　domain　based　on　subdomains　of　two　source　domains　to　reduce　distribution　differences　among　relevant　subdomains　and　global　domain.　Finally,　the　proposed　method　was　experimentally　verified　using　the　bearing　dataset　of　Case　Western　Reserve　University,　US　and　the　actual　bearing　vibration　dataset　generated　by　MFS　mechanical　comprehensive　fault　test　bench.　The　results　showed　that　the　average　fault　diagnosis　accuracy　of　this　method　under　multi-working　condition　reaches　99.　76%;　compared　with　existing　representative　methods,　the　proposed　method　has　better　fault　diagnosis　effect.　©　2024　Chinese　Vibration　Engineering　Society.　All　rights　reserved.