The　chiller　is　the　core　component　of　the　heating,　ventilation,　and　air　conditioning　(HVAC)　system,　such　that　the　intelligent　fault　diagnosis　of　the　chillers　is　of　great　significance　for　equipment　safety　and　energy　conservation.　The　working　conditions　of　the　chiller　are　complex　and　changeable,　resulting　in　considerable　differences　in　the　distribution　of　their　operating　data.　In　addition,　collecting　fault　data　under　various　working　conditions　is　also　very　costly.　To　address　fault　diagnosis　challenges　without　labeled　data　for　changing　working　conditions,　this　article　proposes　a　domain　adaptive　adversarial　clustering　(DAAC)　algorithm,　which　achieves　fault　diagnosis　under　dynamic　distributions　in　evolving　working　conditions.　Specifically,　the　residual　model　based　on　autoencoders　is　used　to　decouple　features.　Then,　the　feature　representation　can　be　obtained　through　the　data　encoding　network　and　mapped　to　a　unit　sphere　space.　Simultaneously,　a　single-layer　unbiased　neural　network　classifier＇s　weight　matrix　is　initialized　as　the　prototype.　In　addition,　the　target　domain　features　are　stored　in　a　memory　buffer　(MB),　and　a　nonparametric　softmax　classifier　is　adopted　to　calculate　the　similarity　entropy　between　the　target　and　prototypes.　On　this　basis,　the　iterative　adversarial　optimization　is　carried　out　to　achieve　dynamic　clustering　of　prototypes　and　target　domain　features.　Experimental　results　on　the　American　Society　of　Heating,　Refrigerating,　and　Air　Conditioning　Engineers　(ASHRAE)　RP-1043　dataset　and　HY-31C　dataset　show　that　the　self-supervised　adaptive　clustering　method　exhibits　excellent　feature　alignment,　enabling　high-precision　fault　diagnosis　without　labeled　data　for　target　conditions.