Model-based Approach for Multi-sensor Fault Identification in Power Plant Gas Turbines

In this paper, ‎the multi-sensor fault diagnosis in the exhaust temperature sensors of a V94.2 heavy duty gas turbine is presented‎. ‎A Laguerre network-based fuzzy modeling approach is presented to predict the output temperature of the gas turbine for sensor fault diagnosis‎. Due to the nonlinear dynamics of the gas turbine, in these models the Laguerre filter parts are related to the linear dynamic part of the models and ‎the nonlinear parts of models are considered as neuro-fuzzy models. In order to deal with the dimensionality problems associated with fuzzy models‎, ‎the nonlinear parts of models are considered as hierarchical fuzzy systems. In the residual evaluation phase, model error modeling adaptive threshold approach is used to increase fault detection robustness against the noise and disturbance. A new expert fuzzy system by multi-sensor information fusion is presented for the fault diagnosis system‎, ‎which can examine the performance of all the sensors simultaneously‎. The result shows that the proposed fault diagnosis system could considerably increase reliability and safety‎.