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International Journal of Aerospace Engineering
Volume 2016, Article ID 7904657, 15 pages
http://dx.doi.org/10.1155/2016/7904657
Research Article

Online Fault-Tolerant Onboard Aeroengine Model Tuning Structure

1School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China
2School of Transportation Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China

Received 9 July 2016; Revised 7 October 2016; Accepted 19 October 2016

Academic Editor: Kenneth M. Sobel

Copyright © 2016 Shuiting Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. B. Armstrong and D. L. Simon, “Implementation of an integrated on-board aircraft engine diagnostic architecture,” in Proceedings of the 47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, AIAA-2011-5859, San Diego, Calif, USA, August 2011. View at Publisher · View at Google Scholar
  2. T. Kobayashi and D. L. Simon, “Integration of on-line and off-line diagnostic algorithms for aircraft engine health management,” Journal of Engineering for Gas Turbines and Power, vol. 129, no. 4, pp. 986–993, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. A. Turso and J. S. Litt, “A foreign object damage event detector data fusion system for turbofan engines,” Journal of Aerospace Computing, Information and Communication, vol. 2, no. 7, pp. 291–308, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. T. J. Grindle and F. W. Burcham Jr., “Engine damage to a NASA DC-8-72 airplane from a high-altitude encounter with a diffuse volcanic ash cloud,” NASA/TM-2003-212030, 2003. View at Google Scholar
  5. C. Hajiyev and F. Caliskan, “Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering,” Aerospace Science and Technology, vol. 4, no. 6, pp. 415–422, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. F. Caliskan and C. M. Hajiyev, “Aircraft sensor fault diagnosis based on Kalman filter innovation sequence,” in Proceedings of the 37th IEEE Conference on Decision and Control, vol. 2, pp. 1313–1314, Tampa, Fla, USA, December 1998. View at Publisher · View at Google Scholar
  7. T. Kobayashi and D. L. Simon, “Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics,” Journal of Engineering for Gas Turbines and Power, vol. 127, no. 3, pp. 497–504, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Kobayashi and D. L. Simon, “Application of a bank of Kalman filters for aircraft engine fault diagnostics,” in Proceedings of the ASME Turbo Expo 2003, Collocated with the 2003 International Joint Power Generation Conference, vol. 1, pp. 461–470, American Society of Mechanical Engineers, Atlanta, Ga, USA, June 2003. View at Publisher · View at Google Scholar
  9. W. Xue, Y.-Q. Guo, and X.-D. Zhang, “A bank of Kalman filters and a Robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator,” in Proceedings of the 2nd International Conference on Innovative Computing, Information and Control (ICICIC '07), Kumamoto, Japan, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Salahshoor, M. Mosallaei, and M. Bayat, “Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm,” Measurement, vol. 41, no. 10, pp. 1059–1076, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Joerger and B. Pervan, “Kalman filter-based integrity monitoring against sensor faults,” Journal of Guidance, Control, and Dynamics, vol. 36, no. 2, pp. 349–361, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Pourbabaee, N. Meskin, and K. Khorasani, “Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties,” Mechanical Systems & Signal Processing, vol. 76-77, pp. 136–156, 2016. View at Google Scholar
  13. S. Garg, “Controls and health management technologies for intelligent aerospace propulsion systems,” in Proceedings of the 42nd AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2004-949, pp. 11854–11876, Reno, Nev, USA, January 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. J. S. Litt, D. L. Simon, S. Garg et al., “A survey of intelligent control and health management technologies for aircraft propulsion systems,” Journal of Aerospace Computing, Information and Communication, vol. 1, no. 12, pp. 543–563, 2004. View at Google Scholar · View at Scopus
  15. A. Behbahani, S. Adibhatla, and C. Rauche, “Integrated model-based controls and PHM for improving turbine engine performance, reliability, and cost,” in Proceedings of the 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, AIAA 2009-5534, Denver, Colo, USA, August 2009. View at Publisher · View at Google Scholar
  16. T. Kobayashi and D. L. Simon, “Hybrid Kalman filter: a new approach for aircraft engine in-flight diagnostics,” ARL-TR 4001, 2006. View at Google Scholar
  17. T. Kobayashi and D. L. Simon, “Hybrid Kalman filter approach for aircraft engine in-flight diagnostics: sensor fault detection case,” Journal of Engineering for Gas Turbines and Power, vol. 129, no. 3, pp. 746–754, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Garg, “Propulsion controls and diagnostics research at NASA Glenn,” Tech. Rep. AIAA-2007-5713, 2007. View at Publisher · View at Google Scholar
  19. D. L. Simon and S. Garg, “A systematic approach for model-based aircraft engine performance estimation,” in Proceedings of the AIAA Infotech@Aerospace Conference, Infotech@Aerospace Conferences, AIAA-2009-1872, pp. 2009–1872, Seattle, Wash, USA, April 2009. View at Publisher · View at Google Scholar
  20. B. Pourbabaee, N. Meskin, and K. Khorasani, “Multiple-model based sensor fault diagnosis using hybrid Kalman filter approach for nonlinear gas turbine engines,” in Proceedings of the 1st American Control Conference (ACC '13), pp. 4717–4723, IEEE, Washington, DC, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Pourbabaee, N. Meskin, and K. Khorasani, “Sensor fault detection, isolation, and identification using multiple-model-based hybrid Kalman filter for gas turbine engines,” IEEE Transactions on Control Systems Technology, vol. 24, no. 4, pp. 1184–1200, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. J. B. Armstrong and D. L. Simon, “Constructing an efficient self-tuning aircraft engine model for control and health management applications,” in Proceedings of the 2012 Annual Conference of the Prognostics and Health Management Society (PHM '12), NASA/TM-2012-217806, pp. 134–146, Minneapolis, Minn, USA, September 2012. View at Scopus
  23. D. L. Simon and J. B. Armstrong, “An integrated approach for aircraft engine performance estimation and fault diagnostics,” Journal of Engineering for Gas Turbines and Power, vol. 135, no. 7, Article ID 071203, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. A. W. Rinehart and D. L. Simon, “An integrated architecture for aircraft engine performance monitoring and fault diagnostics: engine test results,” in Proceedings of the 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Propulsion and Energy Forum, Cleveland, Ohio, USA, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Volponi, “Enhanced self tuning on-board real-time model (eSTORM) for aircraft engine performance health tracking,” Tech. Rep. FR-26751, 2008. View at Google Scholar
  26. A. Volponi, T. Brotherton, and R. Luppold, “Empirical tuning of an on-board gas turbine engine model for real-time module performance estimation,” Journal of Engineering for Gas Turbines and Power, vol. 130, no. 2, Article ID 021604, pp. 669–678, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. L. C. Jaw and J. D. Mattingly, Aircraft Engine Controls—Design, System Analysis, and Health Monitoring, American Institute of Aeronautics and Astronautics, Reston, Va, USA, 2009.
  28. D. T. Pham, J. Verron, and M. C. Roubaud, “A singular evolutive extended Kalman filter for data assimilation in oceanography,” Journal of Marine Systems, vol. 16, no. 3-4, pp. 323–340, 1998. View at Publisher · View at Google Scholar · View at Scopus
  29. G. A. Dukeman, “Profile-following entry guidance using linear quadratic regulator theory,” in Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, AIAA-2002-4457, Monterey, Calif, USA, August 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Bemporad, M. Morari, V. Dua, and E. N. Pistikopoulos, “The explicit linear quadratic regulator for constrained systems,” Automatica, vol. 38, no. 1, pp. 3–20, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Reberga, D. Henrion, J. Bernussou, and F. Vary, “LPV modeling of a turbofan engine,” in Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, July 2005.
  32. R. Tóth, “Modeling and identification of linear parameter-varying systems,” Lecture Notes in Control and Information Sciences, vol. 403, pp. 1–339, 2010. View at Publisher · View at Google Scholar · View at Scopus