Sensors　are　critical　components　of　heating,　ventilation,　and　air-conditioning　systems.　Sensor　faults　can　impact　control　regulations,　resulting　in　an　uncomfortable　indoor　environment　and　energy　wastage.　To　detect　and　identify　sensor　faults　quickly,　this　study　proposes　an　enhanced　principal　component　analysis　(PCA)　method　using　the　Savitzky–Golay　(SG)　filter　and　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　algorithm.　First,　the　DBSCAN　algorithm　is　used　to　automatically　divide　the　dataset　into　sub-datasets　with　different　working　conditions　to　reduce　the　interference　information　and　concentrate　the　information　of　each　training　set.　Then,　each　sub-dataset　is　smoothed　using　the　SG　algorithm　to　reduce　the　effects　of　data　fluctuations.　The　processed　dataset　is　used　to　build　a　sub-PCA　model　that　ultimately　identifies　and　locates　faults.　The　proposed　strategy　is　validated　using　field　operating　data　for　20　air-handling　unit　(AHU)　systems,　as　obtained　from　a　large　commercial　building.　The　fault　detection　performances　of　multiple　strategies　are　compared　and　analysed　under　different　degrees　of　bias　in　single　AHU　and　multiple　AHU　systems.　The　verification　results　show　that　the　proposed　DBSCAN-SG-PCA　model　offers　significant　improvements　in　fault　detection　accuracy　and　fault　identification　sensitivity　over　the　conventional　PCA　method.　Compared　with　the　SG-PCA　model,　the　proposed　model　reduces　the　amount　of　data　required　for　fault　detection　by　an　average　of　13.7%,　and　the　Youden　index　is　increased　by　an　average　of　0.21.　Furthermore,　the　fault　detection　accuracy　of　the　proposed　model　is　±0.7　°C.　©　2023　Elsevier　Ltd