A　modeling　method　is　proposed　and　applied　in　fault　detection　for　nonlinear　dynamical　systems　with　unknown　but　bounded　noises.　Since　the　Takagi-Sugeno　(T-S)　fuzzy　model　is　a　universal　approximator,　it　is　used　to　model　the　nonlinear　dynamical　system　when　the　system　runs　without　a　fault.　After　some　input　and　output　data　of　the　system　are　obtained,　the　input　space　is　partitioned　using　a　fuzzy　clustering　algorithm.　Assuming　that　the　system　noise　and　approximation　error　are　unknown　but　bounded,　the　consequence　parameters　of　the　T-S　fuzzy　model　of　the　system　are　determined　by　means　of　a　linear-in-parameter　set　membership　estimation　algorithm.　An　interval　containing　the　actual　output　of　the　system　running　without　a　fault　can　be　easily　predicted　based　on　the　result　of　the　estimation.　If　the　measured　output　is　out　of　the　predicted　interval,　it　can　be　determined　that　a　fault　has　occurred.　Simulation　results　show　the　effectiveness　of　the　proposed　method.