A Novel Hybrid Aeroengine Modeling Method for Combining Data-Driven Modules-Reference-Cited by-同舟云学术

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A Novel Hybrid Aeroengine Modeling Method for Combining Data-Driven Modules

Published:2024-09 Issue:5 Volume:37 Page:
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Cai Wen¹,Zhao Yong-Ping²^ORCID,Zhu Ye³,Yin Jun⁴,Xu Zhan-Yan⁴,Liu Wei-Min⁴

Affiliation:

1. Ph.D. Student, College of Energy and Power Engineering, Nanjing Univ. of Aeronautics and Astronautics, Nanjing 210016, China.

2. Professor, College of Energy and Power Engineering, Nanjing Univ. of Aeronautics and Astronautics, Nanjing 210016, China (corresponding author). ORCID: .

3. Senior Researcher, Software Dept., Aero Engine Control System Institute, Wuxi 214063, China.

4. Engineer, Software Dept., Aero Engine Control System Institute, Wuxi 214063, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JAEEEZ.ASENG-5531

Reference30 articles.

1. Alag G. S. and G. B. Gilyard. 1990. “A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine.” In Proc. 26th Joint Propulsion Conf.: AIAA-90-1920. Washington DC: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.1990-1920.

2. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines

3. Direct control of aeroengine thrust based on correlation analysis and neural networks;Chen T.;J. Nanjing Univ. Aeronaut. Astronaut.,2005

4. Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey

5. Long Short-Term Memory

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