Identification of Acoustic Emission Signal of Tank Bottom Corrosion Based on Weighted Fuzzy Clustering

Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 158)

Abstract

The identification of corrosion acoustic emission signal of tank bottom is the basis of improving detection accuracy. However, under some circumstances, the types of corrosion acoustic emission signal of the tank bottom are unknown, and a weighted fuzzy clustering recognition method was proposed to solve this problem. The main characteristics of the signal were given by acoustic emission detector; the characteristics included rise time, count, energy, duration, amplitude, and average frequency. Aiming at the randomness of clustering initialization, nearest neighbor method was used for optimizing initial clustering. For improving the accuracy of edge data, a weighted fuzzy clustering method was proposed to increase the difference of various acoustic emission signals. The data redistribution method of fuzzy clustering is adjusted with the weighted distance between the gravity and center to substitute the traditional distance and then allocated data to the set which had the minimum weighted distance. This study shows that the veracity of weighted fuzzy clustering increases by about 9 %.

Keywords

Petroleum Covariance Acoustics 

Notes

Acknowledgment

This work is supported by the Creative Team Project Foundation of the Education Department of Liaoning Province, China (Grant No. LT2010082).

References

  1. 1.
    S. Ramadan, L. Gaillet, C. Tessier, Detection of stress corrosion cracking of high-strength steel used in prestressed concrete structures by acoustic emission technique [J]. Appl. Surf. Sci. 254, 2255 (2008)CrossRefADSGoogle Scholar
  2. 2.
    A. Cakir, S. Tuncell, A. Alptekin, AE response of 316LSS during SSR test under potentiostatic control [J]. Corros. Sci. 41, 1175 (1999)CrossRefGoogle Scholar
  3. 3.
    M.J. Bennett, D.J. Buttle, P.D. Colledge, Spallation of oxide scales from 20 % Cr-25 N-i-Nb stainless steel [J]. Mater. Sci. Eng. A120, 199 (1989)CrossRefGoogle Scholar
  4. 4.
    W. Jin, C.Z. Chen, Z.H. Jin, B. Gong, B.C. Wen, The three-ratio method of acoustic emission source recognition [J]. Chin. J. Sci. Instrum. 29(3), 530 (2008)Google Scholar
  5. 5.
    G.Y. Gong, Study of FCM algorithms on parameters and its applications. Xi'an University of electronic science and technology [D] (2004)Google Scholar
  6. 6.
    J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum, New York, NY, 1981), pp. 95–107CrossRefMATHGoogle Scholar
  7. 7.
    N.R. Pal, J.C. Bezdek, On cluster validity for the fuzzy for the fuzzy C-means model. IEEE Trans. Fuzzy. Syst. 3(3), 370–379 (1995)CrossRefGoogle Scholar
  8. 8.
    X. Zhang, G. Zhang, P. Liu, Based on the clustering criterion function improved K-means algorithm. Comput. Eng. Appl. 47(11), 123 (2011)Google Scholar
  9. 9.
    S. Nittel, T.L. Kelvin, A. Braverman, Scaling clustering algorithms for massive data sets using data streams [C]. Proceedings of the 20th International Conference on Data Engineering, ICDE’04, 2004, p. 830Google Scholar
  10. 10.
    P.S. Bradley, U. Fayyad, C. Reina, Scaling clustering algorithms to large databases [C]. Proceedings of the 4th ACM SIGKDD, New York, 1998, pp. 9–15Google Scholar
  11. 11.
    H. Shaikh, R. Amirthalingam, T. Anita, Evaluation of stress corrosion cracking phenomenon in an AISI type 316LN stainless steel using acoustic emission technique [J]. Corros. Sci. 49, 740 (2007)CrossRefGoogle Scholar
  12. 12.
    W.K. Wang, Z.M. Zeng, G. Du, Y.J. Wei, S.Z. Song, Clustering analysis of acoustic emission signals during stress corrosion of 304 stainless steel [J]. CIESC Journal. 62(4), 1027 (2011)Google Scholar
  13. 13.
    W.K. Wang, Y.B. Li, G. Du, T. Zhang, S.J. Jin, Based on cluster analysis of acoustic emission signal fusion method at the tank bottom. J. Vibr. Shock. 31(17), 181 (2012)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yang Yu
    • 1
  • Hui Cao
    • 1
  • Ping Yang
    • 1
  • Yuan Fu
    • 1
  • Ling Jun
    • 2
  1. 1.Shenyang University of TechnologyShenyangChina
  2. 2.Drilling and Production TechnologyInstitute of Liaohe OilfieldPanjinChina

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