Most　industrial　processes　feature　high　nonlinearity,　non-Gaussianity,　and　time　correlation.　Models　based　on　overcomplete　broad　learning　system　(OBLS)　have　been　successfully　applied　in　the　fault　monitoring　realm,　which　may　relatively　deal　with　the　nonlinear　and　non-Gaussian　characteristics.　However,　these　models　barely　take　time　correlation　into　full　consideration,　hindering　the　further　improvement　of　the　monitoring　accuracy　of　the　network.　Therefore,　an　effective　dynamic　overcomplete　broad　learning　system　(DOBLS)　based　on　matrix　extension　is　proposed,　which　extends　the　raw　data　in　the　batch　process　with　the　idea　of　＂time　lag＂　in　this　article.　Subsequently,　the　OBLS　monitoring　network　is　employed　to　continue　the　analysis　of　the　extended　dynamic　input　data.　Finally,　a　monitoring　model　is　established　to　tackle　the　coexistence　of　nonlinearity,　non-Gaussianity,　and　time　correlation　in　process　data.　To　illustrate　the　superiority　and　feasibility,　the　proposed　model　is　conducted　on　the　penicillin　fermentation　simulation　platform,　the　experimental　result　of　which　illustrates　that　the　model　can　extract　the　feature　of　process　data　more　comprehensively　and　be　self-updated　more　efficiently.　With　shorter　training　time　and　higher　monitoring　accuracy,　the　proposed　model　can　witness　an　improvement　of　average　monitoring　accuracy　by　3.69%　and　1.26%　in　26　process　fault　types　compared　to　the　state-of-the-art　fault　monitoring　methods　BLS　and　OBLS,　respectively.