Table of Contents Author Guidelines Submit a Manuscript
International Journal of Rotating Machinery
Volume 2018 (2018), Article ID 2095385, 13 pages
https://doi.org/10.1155/2018/2095385
Research Article

Crack Detection of Fan Blade Based on Natural Frequencies

1Beijing Precision Metrology Laboratory, Beijing University of Technology, Beijing 100124, China
2Ennovation System Control Co., Ltd., Beijing 101111, China
3Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100022, China

Correspondence should be addressed to Mengyao Yu; nc.ude.tujb.sliame@oaygnemuy

Received 1 September 2017; Accepted 8 January 2018; Published 5 February 2018

Academic Editor: Jingyin Li

Copyright © 2018 Mengyao Yu 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. S. K. Bhaumik, T. A. Bhaskaran, R. Rangaraju, M. A. Venkataswamy, M. A. Parameswara, and R. V. Krishnan, “Failure of turbine rotor blisk of an aircraft engine,” Engineering Failure Analysis, vol. 9, no. 3, pp. 287–301, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Kim, “Crack evaluation of the fourth stage blade in a low-pressure steam turbine,” Engineering Failure Analysis, vol. 18, no. 3, pp. 907–913, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Wang, Y. Zi, Z. Wan, B. Li, and Z. He, “Effects of multiple cracks on the forced response of centrifugal impellers,” Mechanical Systems and Signal Processing, vol. 60, pp. 326–343, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. A. J. Oberholster and P. S. Heyns, “On-line fan blade damage detection using neural networks,” Mechanical Systems and Signal Processing, vol. 20, no. 1, pp. 78–93, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. S. W. Liu, J. H. Huang, J. C. Sung, and C. C. Lee, “Detection of cracks using neural networks and computational mechanics,” Computer Methods Applied Mechanics and Engineering, vol. 191, no. 25-26, pp. 2831–2845, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. C.-B. Yun and E. Y. Bahng, “Substructural identification using neural networks,” Computers & Structures, vol. 77, no. 1, pp. 41–52, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. F. S. Buezas, M. B. Rosales, and C. P. Filipich, “Damage detection with genetic algorithms taking into account a crack contact model,” Engineering Fracture Mechanics, vol. 78, no. 4, pp. 695–712, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Liu, K. Xin, and Q. Qi, “Study of Structural Damage Detection with Multi-objective Function Genetic Algorithms,” Procedia Engineering, vol. 12, pp. 80–86, 2011. View at Google Scholar
  9. M. Vakil-Baghmisheh, M. Peimani, M. H. Sadeghi, and M. M. Ettefagh, “Crack detection in beam-like structures using genetic algorithms,” Applied Soft Computing, vol. 8, no. 2, pp. 1150–1160, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Ananda Rao, J. Srinivas, and B. S. N. Murthy, “Damage detection in vibrating bodies using genetic algorithms,” Computers & Structures, vol. 82, no. 11-12, pp. 963–968, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Mehrjoo, N. Khaji, and M. Ghafory-Ashtiany, “Application of genetic algorithm in crack detection of beam-like structures using a new cracked Euler-Bernoulli beam element,” Applied Soft Computing, vol. 13, no. 2, pp. 867–880, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. V. T. Tran, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, “Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference,” Expert Systems with Applications, vol. 36, no. 2, pp. 1840–1849, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Lei, Z. He, Y. Zi, and Q. Hu, “Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2280–2294, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. P. M. Pawar and R. Ganguli, “Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2212–2236, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. R. A. Saeed, A. N. Galybin, and V. Popov, “3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS,” Mechanical Systems and Signal Processing, vol. 34, no. 1-2, pp. 259–276, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Maghsoodi, A. Ghadami, and H. R. Mirdamadi, “Multiple-crack damage detection in multi-step beams by a novel local flexibility-based damage index,” Journal of Sound and Vibration, vol. 332, no. 2, pp. 294–305, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. K. Pandey, M. Biswas, and M. M. Samman, “Damage detection from changes in curvature mode shapes,” Journal of Sound and Vibration, vol. 145, no. 2, pp. 321–332, 1991. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Wang, M. Liang, and J. Xiang, “Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information,” Mechanical Systems and Signal Processing, vol. 48, no. 1-2, pp. 351–367, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Nahvi and M. Jabbari, “Crack detection in beams using experimental modal data and finite element model,” International Journal of Mechanical Sciences, vol. 47, no. 10, pp. 1477–1497, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. N. T. Khiem and L. K. Toan, “A novel method for crack detection in beam-like structures by measurements of natural frequencies,” Journal of Sound and Vibration, vol. 333, no. 18, pp. 4084–4103, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. G. M. Owolabi, A. S. J. Swamidas, and R. Seshadri, “Crack detection in beams using changes in frequencies and amplitudes of frequency response functions,” Journal of Sound and Vibration, vol. 265, no. 1, pp. 1–22, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. K. H. Barad, D. S. Sharma, and V. Vyas, “Crack detection in cantilever beam by frequency based method,” Procedia Engineering, vol. 51, pp. 770–775, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. X. F. Yang, A. S. J. Swamidas, and R. Seshadri, “Crack identification in vibrating beams using the energy method,” Journal of Sound and Vibration, vol. 244, no. 2, pp. 339–357, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Poursaeidi and M. Salavatian, “Fatigue crack growth simulation in a generator fan blade,” Engineering Failure Analysis, vol. 16, no. 3, pp. 888–898, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Witek, “Experimental crack propagation and failure analysis of the first stage compressor blade subjected to vibration,” Engineering Failure Analysis, vol. 16, no. 7, pp. 2163–2170, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. W. M. Ostachowicz and M. Krawczuk, “Analysis of the effect of cracks on the natural frequencies of a cantilever beam,” Journal of Sound and Vibration, vol. 150, no. 2, pp. 191–201, 1991. View at Publisher · View at Google Scholar · View at Scopus