A quadratic programming framework for constrained and robust jet engine health monitoring

Abstract

Kalman filters are largely used in the jet engine community for condition monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the residuals between the model prediction and the measurements are zero-mean, Gaussian random variables. In the case of sensor faults, this assumption does not hold anymore and consequently, the diagnosis is spoiled. This contribution presents a recursive estimation algorithm based on a Quadratic Programming (QP) formulation which provides robustness against sensor faults and allows constraints on the health parameters to be specified. The improvements in estimation accuracy brought by this new algorithm are illustrated on a series of typical test-cases that may be encountered on current turbofan engines.