This　paper　proposes　a　new　structural　novelty　detection　method　in　the　case　of　vast　measurement　data　having　uncertainties.　Considering　the　effects　of　measurement　accuracy　and　environmental　variations　on　measurement　variance,　precise　analytical　methods　of　adaptive　confidence　distance　and　measurement　variance　are　presented　on　the　basis　of　statistical　theory,　and　thus　an　adaptive　consensus　data　fusion　algorithm　has　been　firstly　developed　to　deal　with　the　large　volume　of　data　involving　considerable　uncertainties.　The　proposed　adaptive　fusion　algorithm　can　adaptively　choose　sensors　whose　data　will　be　subsequently　fused.　The　algorithm　is　then　incorporated　with　wavelet　analysis　for　the　purpose　of　structural　novelty　detection.　Two　numerical　examples　are　carried　out　to　validate　the　efficiency　and　adaptability　of　the　proposed　method.　The　obtained　results　have　been　compared　with　those　from　other　existing　methods,　which　demonstrate　the　high　efficiency　of　the　proposed　method　in　data　processing　considering　uncertainties　and　unsatisfied　performance　of　some　sensors,　as　well　as　its　accuracy　in　structural　novelty　detection.　The　proposed　method　also　shows　some　robustness　to　noise.