In　the　process　of　industrial　Escherichia　coli　preparation,　process　data　has　both　nonlinear　and　non-Gaussian　characteristics,　making　it　difficult　to　locate　fault　sources　effectively.　Aiming　at　this　problem,　a　modeling　method　based　on　multiway　kernel　entropy　independent　component　analysis　(MKEICA)　is　proposed.　Furthermore,　in　order　to　overcome　the　insufficiency　of　traditional　low-order　monitoring　statistics　(T2,　I2　and　SPE)　to　obtain　non-Gaussian　information,　a　fourthorder　cumulative　monitoring　statistic　method　was　proposed.　In　the　next　place,　through　the　derivation　of　the　fourth　order　cumulative　monitoring　statistic,　the　cause　of　the　fault　was　obtained.　For　industrial　validation,　the　feasibility　and　superiority　of　the　proposed　monitoring　method　were　demonstrated　in　the　comparison　with　the　multiway　kernel　independent　component　anlaysis　(MKICA)　monitoring　model.　©　2020,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.