Fault　monitoring　of　multiphase　batch　process　is　a　difficult　problem　in　multivariate　statistical　process　monitoring.　It　needs　to　consider　not　only　the　process　monitoring　under　stable　mode,　but　also　the　transition　mode　with　strong　dynamic　nonlinearity.　Since　the　data　has　different　correlations　under　different　operating　modes,　it　is　necessary　to　establish　different　monitoring　models　for　each　process　mode,　especially　the　transition　process　between　stable　modes.　The　biggest　feature　is　the　dynamic　characteristics　of　the　variables.　This　feature　can　be　better　reflected　in　this　transition　using　a　time-varying　covariance　instead　of　a　fixed　covariance　during　the　transition　phase.　In　this　paper,　a　new　strategy　for　batch　process　sub-phase　partition　and　process　monitoring　is　proposed.　Firstly,　the　three-dimensional　data　matrix　is　expanded　into　a　new　two-dimensional　data　according　to　the　time　slice　expansion　strategy.　Secondly,　the　data　of　each　time　slice　is　transformed　by　Kernel　Entropy　Component　Analysis　(KECA),　and　then　the　production　process　is　divided　into　phases　according　to　the　spatial　angle　of　the　kernel　entropy.　The　production　operation　process　is　divided　into　a　stable　phase　and　a　transition　phase,　and　monitoring　models　are　respectively　established　to　monitor　the　production　process;　Finally,　the　application　of　the　penicillin　fermentation　simulation　platform　shows　that　the　Sub-MKECA　phase　partition　results　can　reflect　the　mechanism　of　the　batch　process　well,　and　the　fault　monitoring　of　the　process　shows　that　it　can　detect　faults　in　time　and　accurately,　and　has　high　practicality　value.