A fuzzy system that automatically develops its rule base from a linearized performance model of the engine by selecting the membership functions and number of fuzzy sets is developed in this study to perform gas turbine fault isolation. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line ‘good engine’. A genetic algorithm is used to tune the fuzzy sets to maximize fault isolation success rate. A novel scheme is developed which optimizes the fuzzy system using very few design variables and therefore is computationally efficient. Results with simulated data show that genetic fuzzy system isolates faults with accuracy greater than that of a manually developed fuzzy system developed by the authors. Furthermore, the genetic fuzzy system allows rapid development of the rule base if the fault signatures and measurement uncertainties change. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis neural network is also used to preprocess the measurements before fault isolation. The radial basis network shows significant noise reduction and when combined with the genetic fuzzy system leads to a diagnostic system that is highly robust to the presence of noise in data.

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