An Automatic Pipeline Monitoring System Based on PCA and SVM
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An Automatic Pipeline Monitoring System Based on PCA and SVM

Authors: C. Wan, A. Mita

Abstract:

This paper proposes a novel system for monitoring the health of underground pipelines. Some of these pipelines transport dangerous contents and any damage incurred might have catastrophic consequences. However, most of these damage are unintentional and usually a result of surrounding construction activities. In order to prevent these potential damages, monitoring systems are indispensable. This paper focuses on acoustically recognizing road cutters since they prelude most construction activities in modern cities. Acoustic recognition can be easily achieved by installing a distributed computing sensor network along the pipelines and using smart sensors to “listen" for potential threat; if there is a real threat, raise some form of alarm. For efficient pipeline monitoring, a novel monitoring approach is proposed. Principal Component Analysis (PCA) was studied and applied. Eigenvalues were regarded as the special signature that could characterize a sound sample, and were thus used for the feature vector for sound recognition. The denoising ability of PCA could make it robust to noise interference. One class SVM was used for classifier. On-site experiment results show that the proposed PCA and SVM based acoustic recognition system will be very effective with a low tendency for raising false alarms.

Keywords: One class SVM, pipeline monitoring system, principal component analysis, sound recognition, third party damage.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331109

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References:


[1] R.J Eiber, R.J.; D.J. Jones, G.S.Kramer, "Outside force causes most natural gas pipeline failures", Oil and Gas Journal, vol.85, issue 11, pp.52-57, March 1987.
[2] D. Hausamann, et.al, "Monitoring of gas transmission pipelines-A customer driven civil UAV application", ODAS Conference, 2003.
[3] JE. Huebler, "Detection of Unauthorized Construction Equipment in Pipeline Right-of-Ways", Technical Report of Gas Technology Institute, 2004.
[4] C. Wan, A. Mita and T.Kume, "An automatic pipeline monitoring system using sound information", Structural Control and Health Monitoring, to be published ; published inially within the online version of the Structual Control and Health Monitoring. DOI : 10.1002/stc.295.
[5] C. Wan and A. Mita, " Recognition of potential danger to buried pipelines based on sounds ", Structural Control and Health Monitoring, to be published.
[6] L. Ma, B. Milner and D. Smith, "Acoustic environment classification", ACM TSLP, vol.3, issue.2, pp.1-22, July 2006.
[7] L. Lu, S.Z. Li, H.J. Zhang, "Content-based audio segmentation using support vector machines", IEEE international conference on Multimedia and Expo, pp.956-959, 2001.
[8] P. Gaunard, C.G. Mubikangiey, C. Couvreur, V. Fontaine, "Automatic classification of environmental noise events by hiddenMarkov model", Proc. IEEE, vol.6, pp.3609-3612, May 1998.
[9] V. Peltonen. J. Tuomi, A. Klapuri, J. Huopaniemi and T. Sorsa, "Computational auditory scene recognition", Proc. IEEE, vol.2, pp.1941-1944, May 2002.
[10] L. Lu, S.Z. Li, H.J. Zhang, "Content-based audio classification and segmentation by using support vector machines", Multimedia Systems, vol.8, no.3, pp.482-492, 2003.
[11] Y.Toyoda, J. Huang, S. Ding and Y. Liu, "Environmental sound recognition by multilayered neural networks", Proc. IEEE, CIT, pp.123-127, Sep 2004.
[12] A.G. Krishna and T.V. Sreenivas, "Music instrument recognition: from isolated notes to solo phrases", Proc. IEEE, vol.4, pp.265-268, May 2004.
[13] R.S. Goldhor, "Recognition of Environment Sounds", Proc.IEEE on Acoustics, Speech, and Signal Processing, Vol.1, pp.149-152, Apr 1993.
[14] R. Unnthorsson, T.P. Runarsson and M.T. Jonsson, "Model selection in one class nu-SVMs using RBF kernels", 16th Conference on Condition Monitoring and Diagnostic, pp.1-11, April 2003.
[15] Principal Component analysis (PCA) or Empirical Orthogonal Function (EOF), Lecture notes, Lunds University. Available: http://aqua.tvrl.lth.se/course/VVR005F/2%20EOF.pdf
[16] Principal Component analysis, notes from Indiana University, Available: http://cheminfo.informatics.indiana.edu/~rguha/writing/notes/stats/node 7.html
[17] Principal Components and Factor Analysis, electronic statistics textbook, StatSoft, Inc, Available : http://www.statsoft.com/textbook/stfacan.html