In　order　to　consider　the　influence　of　the　quality　variables　and　dynamic　characteristics　of　the　fermentation　process　on　the　stage　division,　a　multi-phase　fermentation　process　quality-related　fault　monitoring　method　based　on　the　joint　canonical　variable　matrix　isproposed.　Firstly,　the　3D　data　were　unfolded　along　the　batch　direction.　Canonical　correlation　analysis　(CCA)　was　performed　on　each　time　slice　matrix　to　obtain　the　joint　canonical　variable　matrices,　which　were　equipped　with　process　variables　and　quality　variables,　and　K-mean　algorithm　was　used　to　realize　first　partition.　Then　slow　feature　analysis　(SFA)　was　used　to　extract　the　slow　characteristics　of　the　process　dynamics,　and　the　K-mean　algorithm　was　used　for　the　second　partition.　Finally,　the　production　process　was　divided　into　different　stable　phases　and　transition　phases　through　the　comprehensive　analysis　of　the　two-step　division　results.　The　CCA　monitoring　model　was　established　in　each　phase　after　partition　for　quality-related　fault　detection.　According　to　the　changes　of　static　and　dynamic　characteristics,　this　method　can　accurately　distinguish　the　strong　dynamics　and　the　phase　boundaries　by　a　two-step　partition,　also　eectively　improve　the　accuracy　of　quality-related　fault　monitoring.　The　feasibility　and　effectiveness　of　the　proposed　algorithm　were　illustrated　by　a　penicillin　simulation　platform　and　an　industrial　application　of　E.　coli　fermentation,　respectively.　©　2022,　Editorial　Board　of　CIESC　Journal.　All　right　reserved.