Abstract
The normal operation of the aero-engine control system is inseparable from obtaining accurate sensor measurement signals. Many scholars have done researches on sensor fault diagnosis. In this paper, the state space equation is established based on the component level model of a turbofan engine. And the Kalman filter is designed to diagnose the hard faults of the engine sensors. Then the engine model and fault diagnosis methods are integrated into the full digital simulation platform FWorks for verification.
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References
Kumar A, Viassolo D (2008) Model-based fault tolerant control. NASA/CR, pp 45–49
Wallhagen RE, Arpasi DJ (1974) Self-teaching digital-computer program for fail-operational control of a turbojet engine in a sea-level test stand. Contract 501:24
Naidu S, Zafiriou E, Mc Avoy TJ (1990) Use of neural networks for sensor failure detection in a control system. Control Syst Mag 10(3):49–55
Yin J (2001) Neural network and Kalman filter based fusion fault diagnosis for aero-engine sensor. Comput Meas Control 19(8):1936–1938
Yuan C (2006) Aero-engine adaptive model re-construction under sensor failure. J Aerosp Power 21:195–200
Hu X (2006) Handbook of small and medium-sized aero-engines in the world. Aviation Industry Press, Beijing, pp 443–445
Randal R, Daniel EV (2004) Towards in-flight detection and accommodation of faults in aircraft engines. In: AIAA, p 6463
Zhang X (2016) Design and validation of fault tolerant control system for a turbofan engine. Nanjing University of Aeronautics and Astronautics, Nanjing (2016)
Cao C, Shen J, et al (2017) FWorks: an integrated digital simulation platform for FADEC system. In: Proceedings of the 36th Chinese control conference, pp 10316–10321
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Liu, Y., Wang, J., Hu, Z. (2020). Kalman-Filter-Group Based Aero-Engine Sensors Fault Diagnosis and Verification. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-32-9686-2_13
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DOI: https://doi.org/10.1007/978-981-32-9686-2_13
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Online ISBN: 978-981-32-9686-2
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