Intelligent　fault　diagnosis　for　chiller　is　essential　for　energy　efficiency　optimization　and　health　management　within　Heating,　Ventilation,　and　Air　Conditioning　systems.　Deep　learning-based　chiller　fault　diagnosis　methods　have　demonstrated　competitive　performance.　However,　certain　challenges　remain:　(1)　in　large-scale　chiller　systems,　obtaining　labeled　data　is　costly,　leading　to　scarce　labeled　datasets;　(2)　large　amounts　of　unlabeled　data　have　not　been　fully　explored.　This　paper　proposes　a　semi-supervised　fault　diagnosis　algorithm　based　on　system　complexity　quantification.　Firstly,　a　variational　autoencoder-based　lossless　compressor　is　trained　using　unlabeled　data　in　an　unsupervised　manner.　Subsequently,　Kolmogorov　complexity　is　approximated　via　the　compressor　to　achieve　entropy-based　quantification　of　complexity.　Using　the　normalized　information　distance　algorithm,　an　information　distance　matrix　is　then　calculated,　which　is　combined　with　a　K-nearest　neighbors　classifier　for　fault　diagnosis.　The　proposed　method　is　validated　using　the　ASHRAE　1043-RP　and　HY-31C　datasets,　and　the　experimental　results　indicate　that　the　proposed　method　can　use　the　severe　few　labeled　samples　to　obtain　a　better　diagnostic　accuracy.　©　2025　Elsevier　Ltd