A　novel　monitoring　strategy　based　on　Multi-way　Mean　vector　component　analysis　(MMVCA)　is　proposed　for　the　online　fault　detection　of　batch　process.　The　faults　that　affect　quality　index　are　denoted　as　quality-related　faults,　which　should　be　taken　care　of　as　soon　as　possible.　The　method　is　based　on　dimensionality　reduction　by　preserving　the　squared　length,　and　implicitly　also　the　direction,　of　the　mean　vector　of　the　original　data.　The　optimal　mean　vector　preserving　basis　is　obtained　from　the　spectral　decomposition　of　the　inner-product　matrix,　and　it　is　shown　to　capture　clustering　structure.　Unlike　traditional　Multi-way　Principal　Component　Analysis　(MPCA),　these　axes　are　in　general　not　corresponding　to　the　top　eigenvalues.　The　proposed　algorithm　has　been　applied　in　penicillin　fermentation　system　and　plant　data,　to　verify　the　effectiveness　of　the　method.　©　2014　IEEE.