In　wastewater　treatment　processes,　the　monitoring　of　dissolved　oxygen　sensor　is　the　key　to　ensure　the　quality　of　effluent.　In　this　paper,　a　method　for　fault　detection　of　dissolved　oxygen　sensor　is　proposed　using　set　membership　identification　and　radial　basis　function(RBF)　neural　network.　The　time　series　model　of　KLa5　is　built　by　RBF　neural　network　in　virtue　of　its　universal　approximation　ability.　Considering　the　bounded　modeling　error,　the　set　description　of　the　output　weights　of　the　network　is　obtained　by　linear-in-parameters　set　membership　identification　algorithm.　This　built　model　can　give　a　one-step　prediction　of　the　confidence　interval　of　KLa5　under　the　fault-free　case.　If　the　real　of　KLa5　exceeds　the　predicted　confidence　interval,　a　failure　of　the　dissolved　oxygen　sensor　can　be　determined.　©　2020　IEEE.