Fuzzy multiple inference identification and its application to fault diagnosis of industrial processes

摘要

This paper proposes a new approach for fault detection and isolation (FDI) in nonlinear dynamic processes using multiple model approach. The technique presented concerns the identification and design of nonlinear fuzzy inference system based on Takagi-Sugeno (TS) fuzzy models. A nonlinear dynamic process is, in fact, described as a composition of several TS models selected according to the process operating conditions. This work also addresses a method for the identification and the optimal selection of the local TS models from a sequence of noisy measurements acquired from the process. The FDI scheme adopted to generate residuals uses the nonlinear TS fuzzy model. The developed technique was applied to FDI of the power plant of Pont sur Sambre.