Fault　detection　is　important　in　the　operation　of　wastewater　treatment　process　(WWTP).　In　this　paper,　to　ensure　the　process　safety　and　effluent　qualities,　an　intelligent　fault　detection　(IFD)　method,　based　on　self-organizing　type-2　fuzzy-neural-network　(SOT2FNN)　and　intelligent　identification　method,　was　developed　to　detect　and　identify　different　types　of　sludge　bulking.　The　main　advantages　of　IFD　are　as　follows.　First,　a　data-driven　framework,　based　on　a　data-driven　model　and　an　intelligent　identification　algorithm,　was　developed　to　facilitate　the　fault　diagnosis.　Second,　a　SOT2FNN,　based　on　the　intensity　of　information　transmission　algorithm　and　adaptive　second-order　algorithm,　was　designed　to　predict　the　sludge　volume　index　(SW)　with　high　accuracy　to　provide　necessary　information　for　process　monitoring.　Third,　an　intelligent　identification　method,　using　the　target-related　identification　algorithm　(TRIA),　was　proposed　to　extract　the　correlation　information　to　identify　the　types　of　sludge　bulking.　Finally,　simulations　and　experimental　examples　were　provided　to　confirm　the　effectiveness　of　the　proposed　IFD　method.