The　fault　data　of　bearings　exhibitcd　significant　distribution　discrepancies　under　varying　operating　conditions,　relatively　low　diagnostic　accuracy　was　rcsultcd　in　practical　fault　detection　models.　Additionally,　most　existing　research　on　cross-domain　bearing　fault　diagnosis　primarily　cmphasized　inter-domain　alignment　and　intra-class　comparison,　while　neglecting　the　influences　of　interactions　between　subdomains.　Thcrcforc,　a　cross-domain　fault　diagnosis　method　of　bearings　was　proposed　based　on　Joint　subdomain　contrast　alignment.　In　Order　to　highlight　the　fault　features,　the　bearing　Vibration　signals　were　transformed　into　time-frequency　graph　by　short-time　Fourier　transform,　and　the　fault　features　were　obtaincd　by　inputting　them　into　the　feature　extraction　modulc.　Domain　adaptation　methods　achieved　cross-domain　recognition　by　transferring　knowledge　learned　from　the　source　domain　to　the　target　domain.　During　the　domain　adaptation　processes,　a　Joint　subdomain　contrast　alignment　strategy　was　used　to　bring　samples　from　the　same　subdomain　closer　together　while　separating　samplcs　from　different　subdomains,　which　aligned　the　subdomain　distributions　of　the　same　dass　samplcs　among　the　source　and　target　domains,　thereby　enhancing　the　model＇s　generalization　ability　in　the　target　domain.　Resnet34　was　used　as　the　feature　extraction　network　on　the　modcl　architecture,　and　the　maximum　mean　difference　was　used　at　the　Output　of　the　network　to　align　the　global　distribution　of　the　source　domain　and　the　target　domain.　Compared　with　the　classical　domain　adaptation　methods,　the　experimental　results　on　the　bearing　fault　data　set　of　Case　Western　Reserve　University　shows　that　the　cross-domain　fault　diagnosis　method　of　bearings　based　on　Joint　subdomain　contrast　alignment　has　better　feature　transfer　ability.　©　2025　Chinese　Mechanical　Engineering　Society.　All　rights　reserved.