Multi-kernel　independent　component　analysis　(MKICA)　has　been　widely　used　in　monitoring　non-Gaussian　and　non-linear　processes.　The　technique　uses　only　non-linear　extension　of　linear　independent　component　analysis　(ICA)　by　KPCA　data　whitening.　After　KPCA　data　whitening,　the　data　is　considered　only　to　maximize　data　information　but　not　data　cluster　structure　information.　In　order　to　solve　this　problem,　kernel　entropy　component　analysis　(kernel　entropy　component　analysis,　KECA)　was　proposed　to　replace　KPCA　whitening　in　process　monitoring.　First,　3D　data　is　transformed　into　2D　data　by　AT　expansion.　Second,　data　nonlinearity　was　resolved　during　KECA　whitening.　Third,　ICA　monitoring　model　was　established　for　non-Gaussian　production　process　monitoring.　The　method　was　applied　to　simulation　and　actual　industrial　process　of　Penicillin　fermentation,　which　showed　effectiveness　of　the　method　in　comparison　with　the　MKICA　method.　©　All　Right　Reserved.