Neural-network-based observer for turbine engine parameter estimation

Abstract

Abstract

Accurate estimation of unmeasurable engine parameters such as thrust and turbine inlet temperatures constitutes a significant challenge for the aircraft community. A solution to this problem is to estimate these parameters from the measured outputs using an observer. Currently existing technologies rely on Kalman and extended Kalman filters to achieve this estimation. This paper presents a neural-network-based observer that augments the linear Kalman filter with a neural network to compensate for any non-linearity that is not handled by the linear filter. The implemented neural network is a radial basis function network that is trained offline using a growing and pruning algorithm. The neural-network-based observer is trained and simulated to estimate the high-pressure turbine inlet temperature, thrust, and stall margins at different levels of engine degradation for a two-spool turbofan engine. Simulation results show the ability of the observer to accurately estimate the performance parameters. The advantage of this observer is that it does not need explicit estimation of health parameters to accurately estimate the performance parameters.