This　paper　proposes　a　structural　novelty　detection　method　based　on　consensus　data　substitution,　which　is　used　for　structural　health　monitoring　and　novelty　detection,　particularly　for　cases　when　the　important　data　are　missing　due　to　the　facts　of　disabled　sensors　or　transmission　networks.　Firstly,　the　incomplete　data　is　supplemented　by　using　of　weighting-average　fusion　algorithm,　and　then　the　effect　of　variable　accuracy　in　measurement　and　environmental　change　on　the　variance　in　measurement　is　taken　into　account.　In　virtues　of　redundant　information　processing　and　novelty　detection,　the　adaptive　consensus　fusion　algorithm　and　wavelet　analysis　are　employed　to　integrate,　and　detect　structural　novelty,　consequently　the　structural　novelty　detection　is　implemented　in　incomplete　data　finally.　To　validate　the　proposed　method,　the　missing　data　of　a　five-storey　frame　is　identified　as　an　example,　and　structural　novelty　detection　is　also　carried　out.　A　comparison　is　made　with　the　incomplete　data　and　complete　data　for　the　novelty　detection　of　the　frame.　The　novelty　detection　results　are　compared　with　other　consensus　fusion　algorithms.　The　results　show　that　the　proposed　method　can　effectively　supplement　the　missing　important　data　and　is　of　preferable　novelty　detection　and　robustness.