Fault diagnosis for a turbine engine

We deal with a sophisticated component level model (CLM) simulation of a turbine engine (XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of failures. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by the Levenberg-Marquardt method to train a Takagi-Sugeno fuzzy system to represent the engine. The multiple copies of this nonlinear model, each representing a different failure, are then used to generate error residuals by comparing them to the engine output. In fact, we manage the composition of the set of models with a “supervisor” that ensures the appropriate models are online, and that processes the error residuals to detect and identify faults. The robustness of the approach is analyzed and several simulations are conducted to illustrate the effectiveness of the method