A Methodology to Improve the Robustness of Gas Turbine Engine Performance Monitoring Against Sensor Faults

Abstract

For turbine engine performance monitoring purposes, system identification techniques are often used to adapt a turbine engine simulation model to some measurements performed while the engine is in service. Doing so, the simulation model is adapted through a set of socalled health parameters whose values are intended to represent a faithful image of the actual health condition of the engine. For the sake of low computational burden, the problem of random errors contaminating the measurements is often considered to be zero mean, white, and Gaussian random variables. However, when a sensor fault occurs, the measurement errors no longer satisfy the Gaussian assumption and the results given by the system identification rapidly become unreliable. The present contribution is dedicated to the development of a diagnosis tool based on a Kalman filter whose structure is slightly modified in order to accommodate sensor malfunctions. The benefit in terms of the diagnostic reliability of the resulting tool is illustrated on several sensor faults that may be encountered on a current turbofan layout.