Sludge　bulking　is　very　common　in　wastewater　treatment　process　(WWTP),　which　will　degrade　the　operation　performance　or　even　destroy　the　process.　In　order　to　diagnose　sludge　bulking　accurately,　a　data-knowledge-driven　diagnosis　(DKD)　method　is　proposed　to　identify　the　occurrence　and　cause　variable　in　this　paper.　This　proposed　DKD　method　contains　the　following　advantages.　First,　a　data-driven　detection　model,　using　a　recursive　kernel　principal　component　analysis　(RKPCA)　algorithm,　is　designed　to　capture　the　intrinsic　nonlinear　and　time-varying　characteristic　of　sludge　bulking.　Then,　the　occurrence　of　sludge　bulking　can　be　detected　with　high　accuracy.　Second,　a　DKD　model,　based　on　the　Bayesian　network　(BN),　is　developed　to　extract　the　causality　among　process　variables　to　identify　the　root　cause　variables　of　sludge　bulking.　Then,　the　root　cause　variables　of　sludge　bulking　can　be　diagnosed　to　improve　the　operation　performance　of　WWTP.　Finally,　the　proposed　DKD　method　was　tested　on　the　measured　data　from　a　real　WWTP.　Experimental　results　confirmed　the　effectiveness　of　the　proposed　DKD　method.　©　2021　Elsevier　Ltd