Neuro‐fuzzy uncertainty de‐coupling: a multiple‐model paradigm for fault detection and isolation

In this paper, a neuro-fuzzy and de-coupling fault diagnosis scheme (NFDFDS) is proposed for fault detection and isolation (FDI) of nonlinear dynamic systems. In this approach, powerful approximation and reasoning capabilities of neuro-fuzzy models are combined with the de-coupling capabilities of optimal observers to perform reliable fault detection and isolation. The neuro-fuzzy model presented here is a special form of Takagi–Sugeno (TS) fuzzy model used to represent the system by a fuzzy fusion of local linear sub-models. The necessary condition for the application of this FDI scheme is that this special form of the TS model can represent the nonlinear system, which is true for many practical systems. It is shown that if all the local models are stable and the corresponding local observers converge to the local models it can be expected that the global model is stable and the corresponding global observer will converge to the nonlinear input–output system. An application of FDI for an electro-pneumatic valve actuator in a sugar factory is presented. Key issues of finding a suitable structure for detecting and isolating nine realistic actuator faults are described. Copyright © 2004 John Wiley & Sons, Ltd.