Table of Contents Author Guidelines Submit a Manuscript
Abstract and Applied Analysis
Volume 2014, Article ID 902304, 9 pages
http://dx.doi.org/10.1155/2014/902304
Research Article

Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique

1College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
2College of Information and Telecommunication, Harbin Engineering University, Harbin, Heilongjiang 150001, China
3School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Received 17 April 2014; Revised 4 June 2014; Accepted 4 June 2014; Published 23 June 2014

Academic Editor: Hamid Reza Karimi

Copyright © 2014 Zhilin Liu 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. A. A. Doubov, “Diagnostics of equipment and constructions strength with usage of magnetic memory,” Inspection Diagnostics, no. 6, pp. 19–29, 2001. View at Google Scholar
  2. A. A. Dubov and K. Sergey, “The metal magnetic memory method application for online monitoring of damage development in steel pipes and welded joints specimens,” Welding in the World, vol. 57, no. 1, pp. 123–136, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. A. A. Doubov, “Express method of quality control of a spot resistance welding with usage of metal magnetic memory,” Welding in the World, vol. 46, no. 6, pp. 317–320, 2002. View at Google Scholar · View at Scopus
  4. A. A. Dubov, “Development of a metal magnetic memory method,” Chemical and Petroleum Engineering, vol. 47, no. 11, pp. 837–839, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Yin, G. Wang, and H. R. Karimi, “Data-driven design of robust fault detection system for wind turbines,” Mechatronics, vol. 24, no. 4, pp. 298–306, 2014. View at Publisher · View at Google Scholar
  6. S. Yin, S. X. Ding, A. H. A. Sari, and H. Hao, “Data-driven monitoring for stochastic systems and its application on batch process,” International Journal of Systems Science, vol. 44, no. 7, pp. 1366–1376, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6418–6428, 2014. View at Publisher · View at Google Scholar
  8. S. Yin, H. Luo, and S. X. Ding, “Real-time implementation of fault-tolerant control systems with performance optimization,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2402–2411, 2014. View at Publisher · View at Google Scholar
  9. S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 22, no. 9, pp. 1567–1581, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Zarei, M. A. Tajeddini, and H. R. Karimi, “Vibration analysis for bearing fault detection and classification using an intelligent filter,” Mechatronics, vol. 24, no. 2, pp. 151–157, 2014. View at Publisher · View at Google Scholar
  11. J. Zhang, M. Lyu, H. R. Karimi, P. Guo, and Y. Bo, “Robust H filtering for a class of complex networks with stochastic packet dropouts and time delays,” The Scientific World Journal, vol. 2014, Article ID 560234, 11 pages, 2014. View at Publisher · View at Google Scholar
  12. J. Zhang, H. R. Karimi, Z. Zheng, M. Lyu, and Y. Bo, “H filter design with minimum entropy for continuous-time linear systems,” Mathematical Problems in Engineering, vol. 2013, Article ID 579137, 9 pages, 2013. View at Publisher · View at Google Scholar
  13. Y. Liu, J. Suo, H. R. Karimi, and X. Liu, “A filtering algorithm for maneuvering target tracking based on smoothing spline fitting,” Abstract and Applied Analysis, vol. 2014, Article ID 127643, 6 pages, 2014. View at Publisher · View at Google Scholar
  14. Y. D. Song, Q. Cao, X. Du, and H. R. Karimi, “Control strategy based on wavelet transform and neural network for hybrid power system,” Journal of Applied Mathematics, vol. 2013, Article ID 375840, 8 pages, 2013. View at Publisher · View at Google Scholar
  15. H. R. Karimi, M. Zapateiro, and N. Luo, “Application of adaptive wavelet networks for vibration control of base isolated structures,” International Journal of Wavelets, Multiresolution and Information Processing, vol. 8, no. 5, pp. 773–791, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Roskosz, “Metal magnetic memory testing of welded joints of ferritic and austenitic steels,” NDT & E International, vol. 44, no. 3, pp. 305–310, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Yan, J. Zhang, G. Feng, and J. Chen, “Inspection of wet steam generator tubes based on metal magnetic memory method,” Procedia Engineering, vol. 15, pp. 1140–1144, 2011. View at Publisher · View at Google Scholar
  18. Z. D. Wang, K. Yao, B. Deng, and K. Q. Ding, “Theoretical studies of metal magnetic memory technique on magnetic flux leakage signals,” NDT & E International, vol. 43, no. 4, pp. 354–359, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. D. Wang, K. Yao, B. Deng, and K. Q. Ding, “Quantitative study of metal magnetic memory signal versus local stress concentration,” NDT & E International, vol. 43, no. 6, pp. 513–518, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Yao, B. Deng, and Z. D. Wang, “Numerical studies to signal characteristics with the metal magnetic memory-effect in plastically deformed samples,” NDT & E International, vol. 47, pp. 7–17, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Leng, Y. Liu, G. Zhou, and Y. Gao, “Metal magnetic memory signal response to plastic deformation of low carbon steel,” NDT & E International, vol. 55, pp. 42–46, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Hai-Yan, W. Wen-Jiang, W. Ri-Xing, X. Min-Qiang, and L. Xue-Feng, “Stress distribution testing of 50 MW turbine fracture blade with metal magnetic memory method,” Proceedings of the Chinese Society of Electrical Engineering, vol. 26, no. 4, pp. 72–76, 2006. View at Google Scholar · View at Scopus
  23. H. Y. Kang, Y. Lu, and W. Y. Shuzi, “Some algorithms for nondestructive testing of wire ropesłsignal pre-processing and character extraction,” Nonde Structive Testing, vol. 22, no. 11, pp. 483–488, 2000. View at Google Scholar
  24. J. Zhang, B. Wang, and B. Ji, “Signal processing for metal magnetic memory testing of borehole casing based on wavelet transform,” Acta Petrol Ei Sinica, vol. 27, no. 2, pp. 137–140, 2006. View at Google Scholar · View at Scopus
  25. Z. Xiaoyong and Y. Yinzhong, “Multi-fault diagnosis method on Mallat pyramidal algorithm wavelet analysis,” Control and Decision, vol. 19, no. 5, pp. 592–594, 2004. View at Google Scholar · View at Scopus
  26. R. Manojit, V. Kumar, B. D. Kulkarni, J. Sanderson, M. Rhodes, and M. Vander Stappen, “Simple denoising algorithm using wavelet transform,” American Institute of Chemical Engineers Journal, vol. 45, no. 11, pp. 2461–2466, 1999. View at Google Scholar · View at Scopus
  27. J. A. K. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle, “Weighted least squares support vector machines: robustness and sparce approximation,” Neurocomputing, vol. 48, no. 1, pp. 85–105, 2002. View at Publisher · View at Google Scholar · View at Scopus