Fault detection and isolation for nonlinear processes based on local linear fuzzy models and parameter estimation

An approach for model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. A fuzzy model (Takagi-Sugeno type) of the nominal process provides characteristic features like time constants and static gains in the actual region of operation. Comparing these with features derived by recursive parameter estimation leads to significant symptoms which indicate the state of the system. The practical applicability is illustrated on an industrial scale thermal plant. Here, nine different faults can be detected and isolated continuously over all ranges of operation.