In　the　real-world　batch　process,　the　minor　faults　caused　by　aging　equipment　and　catalyst　failure　have　subtle　difference　from　normal　data,　making　it　difficult　to　monitor　them　timely　with　the　traditional　monitoring　methods　based　on　variance　measurement　and　the　deep　learning　methods　based　on　distance　measurement.　In　this　research,　a　novel　monitoring　approach　based　on　fuzzy　broad　learning　system　(FBLS)　is　employed　to　address　the　aforementioned　issues.　To　fully　extract　the　feature　of　raw　data,　the　Takagi-Sugeno　(TS)　fuzzy　system　is　first　adopted　to　process　the　input　data　in　order　to　identify　minor　faults　effectively.　Incremental　learning　algorithm　is　then　employed　to　reconstruct　network　model　quickly　without　retraining　the　entire　network,　which　contributes　to　better　accuracy　and　lower　computation　complexity　and　achieves　online　fault　monitoring.　After　that,　the　classification　of　monitoring　results　is　visualized　to　evaluate　the　fault　type　intuitively　so　as　to　take　corresponding　remedial　actions　quickly.　Consequently,　this　algorithm　is　conducted　into　the　penicillin　fermentation　platform　and　real　industrial　process.　The　results　demonstrate　that　FBLS　can　better　capture　the　fuzzified　feature　and　quickly　update　monitoring　model　to　accomplish　the　self-increase　of　fault　database　without　retraining　the　entire　process.