In　this　paper,　an　effective　strategy　for　fault　detection　of　sludge　volume　index　(SVI)　sensor　is　proposed　and　tested　on　an　experimental　hardware　setup　in　waste　water　treatment　process　(WWTP).　The　main　objective　of　this　fault　detection　strategy　is　to　design　a　system　which　consists　of　the　online　sensors,　the　SVI　predicting　plant　and　fault　diagnosis　method.　The　SVI　predicting　plant　is　designed　utilizing　a　fuzzy　neural　network　(FNN),　which　is　trained　by　a　historical　set　of　data　collected　during　fault-free　operation　of　WWTP.　The　fault　diagnosis　method,　based　on　the　difference　between　the　measured　concentration　values　and　FNN　predictions,　allows　a　quick　revealing　of　the　faults.　Then　this　proposed　fault　detection　method　is　applied　to　a　real　WWTP　and　compared　with　other　approaches.　Experimental　results　show　that　the　proposed　fault　detection　strategy　can　obtain　the　fault　signals　of　the　SVI　sensor　online.　(C)　2014　Elsevier　Ltd.　All　rights　reserved.