A　large　Proportion　of　batch　processes　commonly　have　traits　of　non-Gaussian　and　nonlinear.　In　this　work,　Multiway　Kernel　Entropy　Independent　Component　Analysis　(MKEICA)　algorithm　was　developed　to　formulate　more　accurate　model　for　process　monitoring　so　as　to　enhance　the　monitoring　performance.　The　original　process　data　with　three-dimension　were　first　expanded　into　two-dimensional　data　matrix　by　using　AT　variable　expansion　method.　The　Kernel　Entropy　Component　Analysis　(KECA)　was　then　employed　to　preprocess　the　data　in　order　to　reduce　data　redundancy.　Such　approach　can　also　retain　the　information　of　cluster　structure　and　maximize　the　essential　characteristics　of　data.　After　that,　a　monitoring　model　of　MKEICA　was　established　for　production　process　monitoring.　Once　a　fault　is　detected,　a　nonlinear　contribution　plots　method　would　be　utilized　to　diagnose　the　fault　variables.　Consequently,　to　illustrate　the　superiority　and　feasibility,　the　proposed　method　was　conducted　on　the　penicillin　simulation　platform　and　the　actual　pharmaceutical　production　process.