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Journal of Control Science and Engineering
Volume 2017, Article ID 1982879, 14 pages
https://doi.org/10.1155/2017/1982879
Research Article

WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor

1College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2College of Aerospace Engineering, Civil Aviation University of China, Tianjin 300300, China
3Tianjin Binhai International Airport, Tianjin 300300, China

Correspondence should be addressed to Yigang Sun; moc.361@gyscuac

Received 13 June 2017; Revised 8 September 2017; Accepted 13 September 2017; Published 26 November 2017

Academic Editor: Zhixing Cao

Copyright © 2017 Zhen Zhao 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. K. Cai L, Y. Sun F, and W. Yao W, “Fault diagnosis and adaptive reconfiguration control for sensors in aeroengine,” DIanguang YU Kongzhi, vol. 16, no. 6, pp. 57–61, 2009. View at Google Scholar
  2. B. Zhao W, “Research on aircraft engine sensor fault diagnosis and signal reconstruction,” Nanjing University of Aeronautics and Astronautics, pp. 34–42, 2011. View at Google Scholar
  3. K. Takahisa and S. Donald L, “Application of a bank of Kalman filters for aircraft engine fault diagnosis,” Atlanta: ASME Turbo Expo Power for Land, Sea, and Air, pp. 1–10, 2003. View at Google Scholar
  4. X. Lishuang, C. Tao, and D. Fang, “Sensor fault diagnosis based on least squares support vector machine online prediction,” in Proceedings of the 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics, (RAM '11), pp. 275–279, Qingdao, China, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Duan, Q. Li, and Y. Zhao, “Fault diagnosis for sensors of aero-engine based on improved least squares support vector regression,” in Proceedings of the 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, (ICNC '11), pp. 1962–1966, Shanghai, China, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Zhang, T. Chen, and W. Li, “Abrupt sensor fault diagnosis based on wavelet network,” in Proceedings of the 2006 IEEE International Conference on Information Acquisition, (ICIA '06), pp. 111–116, Weihai, China, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Wang M and Z. Ren, “a sensor fault diagnosis method research based on wavelet transform and hilbert-huang transform,” Hong Kong: Conference on Measuring Technology and Mechatronics Automation, pp. 81–84, 2013. View at Google Scholar
  8. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990, Budapest, Hungary, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Sun G, J. Liu Y, and Z. Zhan, “Extreme learning machine-based aircraft engine sensor fault diagnosis,” Sensor and Micro System, vol. 33, pp. 23–26, 2014. View at Google Scholar
  10. X. Zhang and L. Wang H, “Have a choice and forgetting mechanism of extreme learning machine in the application of time series prediction,” Acta Physica Sinica, vol. 60, no. 8, Article ID 080504, 2011. View at Google Scholar
  11. N. Liang, G. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 17, no. 6, pp. 1411–1423, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Yin, Y. Zhang T, and N. Li Z, “Fault diagnosis method based on online sequential extreme learning machine,” Vibration, Measurement and Diagnosis, vol. 33, no. 2, pp. 325–329, 2013. View at Google Scholar
  13. B. Yu J and L. Zhu Y, “Transformer fault diagnosis using weighted extreme learning machine,” Computer Engineering and Design, vol. 34, pp. 4340–4344, 2013. View at Google Scholar
  14. B. Y. Li, Q. H. Li, J. K. Wang, and X. H. Huang, “Sensor fault adaptive diagnosis of aero-engines based on ImOS-ELM,” in Hangkong Xuebao, vol. 34, pp. 2316–2324, 10 edition, 2013. View at Google Scholar
  15. B. Mirza, Z. Lin, and K.-A. Toh, “Weighted online sequential extreme learning machine for class imbalance learning,” Neural Processing Letters, vol. 38, no. 3, pp. 465–486, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Mirza, Z. Lin, J. Cao, and X. Lai, “Voting based weighted online sequential extreme learning machine for imbalance multi-class classification,” in Proceedings of the IEEE International Symposium on Circuits and Systems, (ISCAS '15), pp. 565–568, Lisbon, Portugal, May 2015. View at Publisher · View at Google Scholar · View at Scopus