In-Flight Engine Diagnostics and Prognostics Using A Stochastic-Neuro-Fuzzy Inference System

ABSTRACT: One key aspect when developing a robust health management system for turbines is the development of accurate and robust fault classifiers. The paper illustrates the application of a hybrid Stochastic-Neuro-Fuzzy-Inference System to fault diagnostics and prognostics for turbine performance. The random fluctuations of turbine performance parameters in different varying operating conditions are modeled using a multivariate stochastic model. At any time, the fault risk condition is approached as a conditional reliability problem based on the measurement of parameter deviations from the normal operating condition. The paper illustrates the application of the proposed system to a typical aircraft turbofan engine for in-flight engine performance diagnostic and prognostic. 1 TURBINE THROUGH FLOW ANALYSIS MODELING Figure 1 shows a sketch of a typical turbofan engine including the performance parameters considered herein for fault diagnostic and prognostic. Figures 2 and 3 show pressure variations as a function of the high-pressure shaft speed stationary conditions versus highly transient operating conditions, respectively. It is obvious from these figures that although for slowly varying conditions the pressure closely follows a nonlinear relationship with shaft speed, for highly transient operating conditions the pressure deviates from this nonlinear path due to highly transient conditions and significant changes in the inlet conditions