A study on neuro-fuzzy systems for fault diagnosis

Fault diagnosis can be facilitated by using either quantitative or qualitative information of the system monitored. This paper presents a novel approach to integrate quantitative and qualitative information in fault-diagnosis, based on the use of neuro-fuzzy systems. In this approach the diagnostic signals (residuals) are generated and evaluated via a B-Spline functions network. The configuration adopted allows the designer to both extract and include symbolic knowledge from the trained network to provide reliable diagnostic information. The effectiveness of the proposed diagnosis strategy is illustrated through a simulation study of a nonlinear two-tank system.