Conventional　independent　component　analysis　(ICA)　monitoring　methods　extract　the　feature　information　of　process　data　by　selecting　more　important　independent　components　(ICs),　which　discard　a　small　part　of　ICs　that　may　contain　useful　information　for　faults,　leading　to　unsatisfactory　monitoring　results.　However,　when　the　number　of　sampling　points　is　greater　than　that　of　process　variables,　the　ICA　monitoring　model　does　not　work　well.　To　address　the　aforementioned　problems,　a　novel　monitoring　method,　multiphase　enhanced　high-order　information　extraction　(MEHOIE),　is　proposed　in　this　paper.　The　entire　production　process　was　first　divided　into　several　steady　phases　and　transition　phases　by　the　affinity　propagation　(AP)　phase　partitioning　method.　The　enhanced　high-order　information　extraction　(EHOIE)　model　was　then　built　in　each　phase　for　fault　monitoring.　Finally,　the　algorithm　was　applied　in　the　penicillin　simulation　platform　and　industrial　microbial　pharmaceutical　process.　The　flexibility　and　superiority　of　this　algorithm　were　verified　by　comparing　it　with　other　conventional　methods.