Health Monitoring for Jet Engines using Robust Filter based on Nonlinear Model

Abstract

We develop a novel jet engine health monitoring method using a high-fidelity, physics-based simulation model. In jet engine health monitoring, fault detection with a state estimation is frequently carried out. However, a challenge for a filtering-based health monitoring method with a high-fidelity model is the difficulty to analytically obtain the Jacobians, which is required for the extended Kalman filter. Our approach is to derive a linearized model a priori at a fixed operation point, and use a robust filter method to guarantee a reliable health monitoring over a wide range of operation points despite the linearization errors. We validate the proposed method by extensive simulations.