Robust fault diagnosis in a chemical process using multiple model identification

Presents a robust model-based technique for the detection and isolation of sensor faults in a chemical process. The diagnosis system is based on the robust estimation of process outputs. A dynamic non-linear model of the process under investigation is obtained by a procedure exploiting Takagi-Sugeno (T-S) multiple-model fuzzy identification. The combined identification and residual generation schemes have robustness properties with respect to modelling uncertainty, disturbance and measurement noise, providing good sensitivity properties for fault detection and fault isolation. The identified system consists of a fuzzy combination of T-S models to detect changing plant operating conditions. Residual analysis and geometrical tests are then sufficient for fault detection and isolation, respectively. The procedure presented is applied to the problem of detecting and isolating faults in a benchmark simulation of a tank reactor chemical process.