The　wastewater　treatment　plant　that　operates　abnormally　may　lead　to　poor　effluent　quality,　resulting　in　the　destruction　of　the　environment,　and　even　more　serious　situation.　Therefore,　it　is　necessary　to　detect　and　isolate　faults　in　the　wastewater　treatment　plants.　This　paper　proposed　a　method　of　fault　detection　and　isolation　using　interval　model.　Radial　basis　function　(RBF)　neural　network　is　utilized　to　model　the　wastewater　treatment　plant,　and　the　linear　output　weights　of　the　neural　network　are　estimated　by　the　set　membership　identification　algorithm.　After　that,　the　confidence　interval　of　the　predicted　effluent　variables　can　be　obtained,　and　then　the　interval　boundary　is　used　as　the　threshold　for　fault　detection.　After　detecting　the　fault,　based　on　this　interval　model　and　the　Bayesian　reasoning,　the　posterior　probability　of　the　considered　faults　can　be　calculated.　When　the　probability　in　exceed　of　a　certain　threshold,　the　fault　can　be　successfully　isolated.　The　final　experimental　results　verified　the　method.　©　2021　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.