Table of Contents Author Guidelines Submit a Manuscript
International Journal of Aerospace Engineering
Volume 2016, Article ID 1329561, 11 pages
http://dx.doi.org/10.1155/2016/1329561
Research Article

Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

1College of Information and Electrical Engineering, Ludong University, Yantai 264025, China
2Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China

Received 27 January 2016; Accepted 20 July 2016

Academic Editor: Kenneth M. Sobel

Copyright © 2016 Shan Pang 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. X. Yang, W. Shen, S. Pang, B. Li, K. Jiang, and Y. Wang, “A novel gas turbine engine health status estimation method using quantum-behaved particle swarm optimization,” Mathematical Problems in Engineering, vol. 2014, Article ID 302514, 11 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. S. O. T. Ogaji, Y. G. Li, S. Sampath, and R. Singh, “Gas path fault diagnosis of a turbofan engine from transient data using artificial neural networks,” in Proceedings of the 2003 ASME Turbine and Aeroengine Congress, ASME Paper No. GT2003-38423, Atlanta, Ga, USA, June 2003.
  3. L. C. Jaw, “Recent advancements in aircraft Engine Health Management (EHM) technologies and recommendations for the next step,” in Proceedings of the 50th ASME International Gas Turbine & Aeroengine Technical Congress, pp. 683–695, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Osowski, K. Siwek, and T. Markiewicz, “MLP and SVM networks—a comparative study,” in Proceedings of the 6th Nordic Signal Processing Symposium (NORSIG '04), pp. 37–40, June 2004. View at Scopus
  5. M. Zedda and R. Singh, “Fault diagnosis of a turbofan engine using neural networks—a quantitative approach,” in Proceedings of the 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA 98-3602, Cleveland, Ohio, USA, 1998. View at Publisher · View at Google Scholar
  6. C. Romessis, A. Stamatis, and K. Mathioudakis, “A parametric investigation of the diagnostic ability of probabilistic neural networks on turbo fan engines,” ASME 2001-GT-11, 2001. View at Google Scholar
  7. A. J. Volponi, H. DePold, R. Ganguli, and C. Daguang, “The use of kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study,” Journal of Engineering for Gas Turbines and Power, vol. 125, no. 4, pp. 917–924, 2003. View at Publisher · View at Google Scholar · View at Scopus
  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, pp. 985–990, Budapest, Hungary, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Yigang, L. Jingya, and Z. Zhen, “Aircraft engine sensor diagnosis based on extreme learning machine,” Transducer and Microsystem Technologies, no. 33, pp. 23–26, 2014. View at Google Scholar
  11. Y. Li, Q. Li, X. Huang, and Y. Zhao, “Research on gas fault fusion diagnosis of aero-engine component,” Acta Aeronautica et Astronautica Sinica, vol. 35, no. 6, pp. 1612–1622, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. X. Yang, S. Pang, W. Shen, X. Lin, K. Jiang, and Y. Wang, “Aero engine fault diagnosis using an optimized extreme learning machine,” International Journal of Aerospace Engineering, vol. 2016, Article ID 7892875, 10 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion,” Journal of Machine Learning Research, vol. 11, pp. 3371–3408, 2010. View at Google Scholar · View at MathSciNet
  15. R. Salakhutdinov and H. Larochelle, “Efficient learning of deep boltzmann machines,” Journal of Machine Learning Research, vol. 9, pp. 693–700, 2010. View at Google Scholar · View at Scopus
  16. D. Yu and L. Deng, “Deep learning and its applications to signal and information processing [exploratory DSP],” IEEE Signal Processing Magazine, vol. 28, no. 1, pp. 145–154, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Lopes, B. Ribeiro, and J. Gonçalves, “Restricted Boltzmann machines and deep belief networks on multi-core processors,” in Proceedings of the International Joint Conference on Neural Networks, pp. 1–7, Brisbane, Australia, June 2012. View at Publisher · View at Google Scholar
  18. N. Le Roux and Y. Bengio, “Deep belief networks are compact universal approximators,” Neural Computation, vol. 22, no. 8, pp. 2192–2207, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  19. H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio, “An empirical evaluation of deep architectures on problems with many factors of variation,” in Proceedings of the 24th International Conference on Machine Learning (ICML '07), pp. 473–480, Corvallis, Ore, UA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Le Roux and Y. Bengio, “Representational power of restricted Boltzmann machines and deep belief networks,” Neural Computation, vol. 20, no. 6, pp. 1631–1649, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  21. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–27, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, “Representational learning with extreme learning machine for big data,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 31–34, 2013. View at Google Scholar
  23. J. Sun, C.-H. Lai, W.-B. Xu, Y. Ding, and Z. Chai, “A modified quantum-behaved particle swarm Optimization,” in Proceedings of the 7th International Conference on Computational Science, pp. 294–301, Beijing, China, May 2007.
  24. M. Xi, J. Sun, and W. Xu, “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 751–759, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. Q.-Y. Zhu, A. K. Qin, P. N. Suganthan, and G.-B. Huang, “Evolutionary extreme learning machine,” Pattern Recognition, vol. 38, no. 10, pp. 1759–1763, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  26. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998. View at Publisher · View at Google Scholar · View at Scopus