As　the　main　concerns　of　abnormal　event　management　in　process　engineering,　fault　detection　and　diagnosis　have　attracted　more　and　more　attention　recently.　A　new　monitoring　method　based　on　kernel　entropy　component　analysis(KECA)　is　proposed　for　nonlinear　chemical　process.　Then,　an　angle-based　statistic　is　designed　to　express　the　distinct　angular　structure　that　KECA　reveals,　which　is　able　to　measure　the　similarity　between　probability　density　functions.　Likewise,　each　KECA　classifier　is　dedicated　to　a　specific　fault,　which　provides　an　expendable　framework　for　incorporating　new　faults　identified　in　the　process.　As　to　the　fault　features　are　submerged　because　of　multi-scale　property　of　process　data,　an　enhanced　KECA　method　for　fault　detection　and　diagnosis　is　developed,　by　adding　multi-scale　principal　component　analysis(MSPCA)　for　features　extraction　to　improve　the　classification　effect　of　KECA.　The　effectiveness　of　the　proposed　approach　is　demonstrated　by　applying　to　Tennessee　Eastman　process.　The　MSPCA　based　method　essentially　captures　the　fault-symptom　correlation,　whereas　KECA　can　be　an　effective　method　for　process　fault　diagnosis.