Research on Fault Diagnosis of PCCP Based on Support Vector Machine

Abstract:

Recent years, many utilities have experienced catastrophic rupture of critical Prestressing Concrete Cylinder Pipe (PCCP) lines throughout the world. Much attention has been focused on reliably assessing the condition of PCCP mains. However, assessment of embedded prestressing wire is difficult. Continuous acoustic monitoring can provide a means of locating problematic pipe sections. In this paper the application of support vector machine (SVM) in acoustic signal detection is proposed. And the effect of this method is investigated. Some key parameters of SVM and kernel functions are surveyed. SVM methods are more effective, especially for the case of lack of training samples. The experiment shows that the SVM method has good classification ability and robust performances. The techniques can provide the opportunity to identify problematic pipe sections and repair the pipe prior to failure. Therefore it can help to prolong the life of a suspect pipeline while minimizing the potential for catastrophic failure.

• Periodical: Advanced Materials Research (Volumes 108-111)
• Pages: 409-414
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.108-111.409
• Online since: May 2010
• Authors: Chun Ting Yang, Yang Liu
• Keywords: Acoustic Monitoring, Prestressed Concrete Cylinder Pipe (PCCP), Support Vector Machine (SVM)

References

[1] N. M. Chowdhury, Z. Liao, L. Zhao and Chunting Yang, Tube Wave Analysis of Buried Pipe, Canadian Acoustic, vol. 37, no. 2(2009), p.25.

[2] R. Wu, Z. Liao, and L. Zhao: Wavelets application in acoustic emission signal detection of wire related events in pipeline, Canadian Acoustic, vol. 36, no. 2(2008), p.33.

DOI: 10.1109/ccece.2008.4564731

[3] M. S. Higgins1 and P. O. Paulson2: Fiber Optic Sensors for Acoustic Monitoring of PCCP, Pure Technologies Ltd. Technical Paper20080212082023.

[4] Bruce Johnson, Myron shenkiryk: Condition-based Asset Management for Critical Water Pipelines, Pure Technologies Ltd. Technical Paper 20080212081809.

[5] Chunting Yang, Yang Liu and Jing Yu: A Framework of Web-GIS for PCCP Risk Management System, CASE2009. pp.480-483.

[6] Chunting Yang, Yang Liu and Jing Yu: Prestressing Concrete Cylinder Pipe Monitoring Based on WSN. Journal of Information and Communication Technology. Vol. 2, No. 2, (2009) pp.58-62.

[7] M G Genton. Classes of kernels for machine learning: a statistics perspective. Journal of Machine Learning Research, 2001; 2: 299~312.

[8] John Shawe-Taylor and Nello Cristianini: Kernel Methods for Pattern Analysis. New York(2004), Cambridge University Press.