Multiple　phases　with　phase　to　phase　transitions　are　important　characteristics　of　many　batch　processes.　The　linear　characteristics　between　phases　are　taken　into　consideration　in　the　traditional　algorithms　while　nonlinearities　are　neglected,　which　can　lead　to　inaccuracy　and　inefficiency　in　monitoring.　The　focus　of　this　paper　is　nonlinear　multi-phase　batch　processes.　A　similarity　metric　is　defined　based　on　kernel　entropy　component　analysis　(KECA).　A　KECA　similarity-based　method　is　proposed　for　phase　division　and　fault　monitoring.　First,　nonlinear　characteristics　can　be　extracted　in　feature　space　via　performing　KECA　on　each　preprocessed　time-slice　data　matrix.　Then　phase　division　is　achieved　with　the　similarity　variation　of　the　extracted　feature　information.　Then,　a　series　of　KECA　models　and　slide-KECA　models　are　established　for　steady　and　transitions　phases　respectively,　which　can　reflect　the　diversity　of　transitional　characteristics　objectively　and　preferably　deal　with　the　stage-transition　monitoring　problem　in　multistage　batch　processes.　Next,　in　order　to　overcome　the　problem　that　the　traditional　contribution　plot　cannot　be　applied　to　the　kernel　mapping　space,　a　nonlinear　contribution　plot　diagnosis　algorithm　is　proposed,　which　is　easier,　more　intuitive　and　implementable　compared　with　the　traditional　one.　Finally,　simulations　are　performed　on　penicillin　fermentation　and　industrial　application.　Specifically,　the　proposed　method　detects　the　abnormal　agitation　power　and　the　abnormal　substrate　supply　at　47　h　and　86　h,　respectively.　Compared　with　traditional　methods,　it　has　better　real-time　performance　and　higher　efficiency.　Results　demonstrate　the　ability　of　the　proposed　method　to　detect　faults　accurately　and　effectively　in　practice.