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Pipeline Leakage Recognition Based on the Projection Singular Value Features and Support Vector Machine

[+] Author Affiliations
Mingda Wang, Laibin Zhang, Wei Liang, Jinqiu Hu

China University of Petroleum, Beijing, China

Paper No. IPC2010-31087, pp. 471-476; 6 pages
  • 2010 8th International Pipeline Conference
  • 2010 8th International Pipeline Conference, Volume 3
  • Calgary, Alberta, Canada, September 27–October 1, 2010
  • Conference Sponsors: International Petroleum Technology Institute and the Pipeline Division
  • ISBN: 978-0-7918-4422-9 | eISBN: 978-0-7918-3885-3
  • Copyright © 2010 by ASME


Identification of negative pressure waveform is the key of pipeline leakage detection. The feature extraction and the choice of the classifier are two main contents to solve the recognition problem. In this paper, a new feature extraction method based on the Projection Singular Value is presented. First of all, the two orthogonal singular value decomposition matrixes of the typical leakage waveform are extracted as the standard bases. Then the projection singular value features of the other pressure wave matrixes are extracted by being projected to the two standard bases. As the pipeline leakage is a small probability event, it is difficult to obtain the leakage samples. A multi-classification Support Vector Machine, which has the advantage of small sample learning, is constructed to classify these features in this paper. The field experiments indicate that the leakage detection based on this feature extraction and recognition model has a higher accuracy of leakage recognition.

Copyright © 2010 by ASME



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