The　extreme　unbalance　of　training　samples　among　different　working　conditions　caused　by　complex　and　variable　external　environment　makes　the　fault　diagnosis　of　chiller　based　on　domain　adaptation　(DA)　poor　performance.　Although　recently　emerging　fault　diagnosis　methods　based　on　domain　generalization　(DG)　can　learn　domain-invariant　knowledge　from　multiple　source　domains　and　can　generalize　to　unseen　target　domains,　these　methods　still　rely　on　multiple　similar　source　domain　data　and　rarely　consider　how　to　enhance　the　ability　to　distinguish　the　joint　distribution　of　features　extracted　from　the　source　domain　samples.　To　address　these　problems,　a　generative　domain-generalized　framework　for　chillers　fault　diagnosis,　namely,　vision　transformer　generative　adversarial　domain　generalization　(VIT-GADG),　is　proposed.　In　VIT-GADG,　a　novel　VIT　domain　generation　network　(VIT-DGN)　is　firstly　designed　to　reduce　DG’s　dependence　on　multi-source　domain　data　by　improving　the　diversity　of　the　distribution　of　the　source　domain　samples.　Then,　a　new　adversarial　domain　generalization　network　called　VIT　conditional　adversarial　domain　generalization　network　(VIT-CADGN)　is　designed　to　extract　domain-invariant　knowledge　from　source　domain　and　latent　domains　that　can　be　generalized　to　unseen　target　domains.　Specifically,　VIT　module　can　effectively　extract　the　global　statistical　feature　of　input　samples,　which　is　conducive　to　the　identification　of　joint　distribution.　Simultaneously,　the　collaborative　conditional　domain　discrimination　strategy　is　introduced　to　improve　the　distribution　discrimination　ability　of　the　extracted　global　statistical　features　while　simultaneously　aligning　its　conditional　distribution.　In　addition,　a　personalized　adaptive　weight　strategy　is　proposed　to　improve　the　performance　of　VIT-CADGN.　Finally,　the　comprehensive　case　study　shows　that　VIT-GADG　has　a　satisfactory　ability　to　extract　invariant　features,　which　improves　the　diagnosis　accuracy　in　the　unseen　target　domain.　IEEE