A MODEL-BASED APPROACH FOR GAS TURBINE ENGINE PERFORMANCE OPTIMAL ESTIMATION

摘要

Kalman filters (KF) are widely used in model-based fault diagnosis designs for aircraft engine health management purposes. Nevertheless, health parameter estimation based on KF in the self-tuning on-board real-time model (STORM) cannot always achieve sufficient health monitoring due to the interrelation of different types of sensors and the number of sensors usually being less than the number of health parameters on the aircraft engine. In order to mitigate this problem, one must accomplish two things: the sensor measurements for fault diagnosis should be analyzed and determined and a health parameter vector in STORM of lower dimension should be acquired. In this paper, a measurement vector for STORM is selected by the concept of sensor condition number and the sensor similarity is used for validation. A transformation matrix for the health parameter vector is introduced, and quantum particle swarm optimization (QPSO) is employed to produce a health parameter subset with appropriate dimensions to enable KF based estimation. Simulations on this method are carried out on a turbofan engine, and the results show that the proposed method for health parameters estimation is efficient.