Hybrid State Estimation for Aircraft Engine Anomaly Detection and Fault Accommodation
Feng Lü, LI Zhi-hu, Jinquan Huang, Mingming Jia
Abstract
This paper is concerned with a novel state estimator to track gas path performance in real time with one sensor failure and packet dropouts for aircraft engine in an advanced distributed architecture. It is common to sensor measurement lost in the distributed network, which results in the decrease of state tracking accuracy. A hybrid extended Kalman filter (HEKF) is proposed for engine performance anomaly detection and sensor fault accommodation from previous studies. Five groups of sensor measurement are divided along gas path related to five local filters, and the local estimated results are fused in the main filter. The reception state matrix is introduced to HEKF to deal with packet dropout, and nonlinear calculation is separated from a local filter to reduce computational burden in the field. Besides, fault diagnosis and isolation strategy of sensor subsets is developed and combined to HEKF by state consistency strategy of distributed network. The contribution of this study is to provide the novel HEKF algorithm to achieve real-time state estimation for sensor-fault-tolerant monitoring of aircraft engine with packet dropout in the distributed structure. The simulation and comparison are systematically carried out, and the superiority of the proposed methodology is confirmed.