Aero-Engine Condition Monitoring Based on Kalman Filter Theory

Abstract:

The maintenance and management of civil aero-engine require advanced monitor schemes to evaluate aero-engine health and condition in order to ensure safety of aircraft and increase life of aero-engine. In this paper, we adopted Kalman filter approach to monitor an aero-engine health and condition by building prediction models of main aero-engine performance parameters (EGT, N1, N2 and WF). The AR model is introduced into the Kalman filter equations, which is a helpful technique to improve the accuracy of monitoring models of performance parameters. When the relative error goes beyond ±0.3%, alarms will be given. The prediction results show that Kalman filter theory using for AR regression prognostic is an effective approach in aero-engine monitoring.

• Periodical: Advanced Materials Research (Volumes 490-495)
• Pages: 176-181
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.490-495.176
• Online since: March 2012
• Authors: You Gao, Nan Wang
• Keywords: Aero-Engine Condition Monitoring, Autoregressive AR Model, Kalman Filter Theory, Performance Parameters, Regression Prediction

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