Total　kernel　projection　to　latent　structures　(T-KPLS)　has　been　widely　used　in　the　fault　detection　control　field,　its　core　idea　is　to　conduct　the　covariance　matrix　decomposition　of　the　data　matrix,　without　using　the　higher-order　statistics　and　other　useful　information　of　the　data,　which　will　cause　an　information　loss　in　the　feature　extraction　process,　then　result　in　a　bad　fault　recognition　performance.　Aiming　to　solve　the　problem,　a　statistics　pattern　analysis　(SPA)　combing　with　the　T-KPLS　based　multi-way　statistics　pattern　analysis　total　kernel　projection　to　latent　structures　(MSPAT-KPLS)　is　proposed.　First,　different　order　statistics　of　the　data　samples　are　constructed　to　map　the　data　from　the　original　data　space　into　the　statistic　sample　space,　then　utilize　kernel　function　to　map　the　statistic　sample　space　into　the　higher　dimensional　kernel　space,　and　according　to　the　quality　variable,　the　feature　space　will　be　divided　into　4　subspaces,　namely:　process　variable　related　to　quality　variable　space,　process　variable　not　related　to　quality　variable　space,　process　variable　orthogonal　to　quality　variable　space　and　residual　error　space;　Lastly,　aiming　at　the　process　variable　related　to　quality　variable　subspace　and　the　residual　error　space,　different　detection　models　are　constructed,　which　will　trace　the　fault　variables　when　faults　are　detected.　In　the　end,　apply　the　proposed　method　on　the　microbial　fermentation　process,　and　the　comparison　results　with　the　traditional　methods　show　that　the　proposed　method　could　achieve　a　better　detection.　©,　2015,　Chemical　Industry　Press.　All　right　reserved.