To　realize　fault　identification　of　unlabeled　data　and　improve　model　generalization　capability,　domain　adaptation　technology　has　been　increasingly　applied　to　intelligent　fault　diagnosis　of　machinery.　Nevertheless,　traditional　domain　adaptation　diagnosis　models　generally　restrict　different　domains　to　have　the　same　label　space,　which　does　not　always　hold　in　complex　industrial　scenarios.　Consequently,　a　more　practical　scenario,　i.e.,　partial-set　transfer　diagnosis,　is　explored　in　this　article,　where　the　target　label　space　is　a　subspace　of　the　source　domain.　A　multiweight　domain　adversarial　network　(MWDAN)　is　proposed　to　solve　this　issue,　in　which　the　weighting　mechanism　of　class-level　and　instance-level　is　jointly　designed　to　distinguish　the　label　space　and　quantify　the　transferability　of　data　samples.　Based　on　the　proposed　strategy,　the　positive　transfer　between　shared　classes　is　promoted　while　the　negative　effect　caused　by　outlier　classes　is　circumvented.　As　a　result,　MWDAN　can　learn　discriminative　representations　for　accurate　fault　diagnosis　in　the　target　domain.　Extensive　experiments　constructed　on　two　mechanical　systems　demonstrate　the　outstanding　performance　of　MWDAN.