The　data　of　sequencing　batch　reactor　(SBR)　has　characteristics　of　non-Gaussian　distribution　and　high　nonlinearity,　In　order　to　solve　the　problem　that　SBR　process　monitoring　algorithm　can　only　maximize　the　use　of　data　information　and　ignore　the　information　in　the　structure　of　data　cluster,　a　new　multi-way　kernel　entropy　component　analysis　(MKECA)　method　is　proposed.　It　also　address　the　shortcomings　of　the　traditional　monitoring　method　in　omission　failure　rate.　A　novel　contribution　analysis　scheme　named　bar　plot　is　developed　for　MKEICA　to　diagnose　faults.　The　proposed　MKEICA　method　consist　of　three　steps:　1)　the　three-dimensional　data　of　SBR　is　unfolded　into　two-dimensional　by　a　new　data　expanding　method.　2)　kernel　entropy　principal　component　analysis　(KEPCA)　is　adopted　to　map　the　two-dimensional　data　into　a　high　dimensional　feature　space　and　use　independent　component　analysis　(ICA)　to　extract　independent　components　(ICs)　in　feature　space.　3)　in　the　stage　of　online　monitoring,bar　plot　is　used　to　identify　the　variables　causing　the　fault.　This　method　is　successfully　applied　to　an　80L　lab-scale　SBR,　and　the　experimental　results　demonstrate　that,　comparing　with　traditional　MKICA,　the　proposed　MKEICA　method　exhibits　better　performance　in　fault　detection　and　diagnose.　©　2019,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.