In　the　real　industrial　production　process,　some　minor　faults　are　difficult　to　be　detected　by　multivariate　statistical　analysis　methods　with　mean　and　variance　as　detection　indicators　due　to　the　aging　equipment　and　catalyst　deactivation.　With　structural　characteristics,　deep　neural　networks　can　better　extract　data　features　to　detect　such　faults.　However,　most　deep　learning　models　contain　a　large　number　of　connection　parameters　between　layers,　which　causes　the　training　time-consuming　and　thus　makes　it　difficult　to　achieve　a　fast-online　response.　The　Broad　Learning　System　(BLS)　network　structure　is　expanded　without　a　retraining　process　and　thus　saves　a　lot　of　training　time.　Considering　that　different　stages　of　the　batch　production　process　have　different　production　characteristics,　we　use　the　Affinity　Propagation　(AP)　algorithm　to　separate　the　different　stages　of　the　production　process.　This　paper　conducts　research　on　a　multi-stage　process　monitoring　framework　that　integrates　AP　and　the　BLS.　Compared　with　other　monitoring　models,　the　monitoring　results　in　the　penicillin　fermentation　process　have　verified　the　superiority　of　the　AP-BLS　model.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.