Batch　processes　have　the　characteristic　of　more　operation　phases　in　nature.　The　standard　Fuzzy　C-Means　(FCM)　algorithm　for　phase　partition　of　batch　processes　needs　to　a　given　phase　partition　number　beforehand,　initialize　clustering　centers　randomly,　and　is　sensitive　to　noise　and　outliers.　For　the　above　problems,　the　adaptive　FCM　algorithm　using　clustering　validity　function　is　proposed　to　achieve　the　adaptive　partition　of　batch　process　operation　phases.　The　method　obtains　the　initial　clustering　center　set　on　the　basis　of　maximum　minimum　distance　rule,　through　adaptive　iteration　way　determines　the　optimal　clustering　number　by　introducing　the　clustering　validity　function.　The　MICA　model　based　on　the　improved　phase　partition　method　is　applied　to　fault　detection　of　industrial　penicillin　fermentation　process　and　the　experimental　results　verify　the　effectiveness　of　the　proposed　method.