Health Monitoring for Jet Engines using Robust Filter based on Nonlinear Model
Yuichi Wakabayashi, Daiki Kakiuchi, Moe Kinoshita, Keiko Nakamura, Mai Kimura, Ryo Sato, Masahiro Ono, Shuichi Adachi
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.
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