Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A multi-time scale vibration surveillance system for third-party threats on urban pipeline

  • Liu, Zelong (College of Civil Engineering, Tongji Univeristy) ;
  • Peng, Renzhu (College of Civil Engineering, Tongji Univeristy) ;
  • Zhang, Yan (College of Civil Engineering, Tongji Univeristy) ;
  • Li, Suzhen (College of Civil Engineering, Tongji Univeristy)
  • Received : 2019.11.07
  • Accepted : 2020.12.29
  • Published : 2021.03.25
⟨ Previous Next ⟩

Abstract

Third-party interference caused by construction activities have seriously jeopardized the security of underground pipelines. Following the process of "signal collection-feature extraction and selection-multi-time scale identifying-combining results by voting", this paper proposes a multi-time scale surveillance system for interference prevention of thirdparty threats on the nearby pipeline by using ground vibration monitors. The system focuses on the two major urban construction activities induced by excavator breaking hammers and road cutters, and presents excellent performance under the noise of traffic and pedestrian. Three features including the short-time zero-crossing rate, subset differential parameter and the Mel frequency cepstrum coefficients are selected by the analysis of the maximal information coefficient and feature importance for identifying the patterns of different third-party activities. The crucial part of the surveillance system consists of the two random forest-based classifiers trained by 0.5 s samples and 8 s samples respectively, and the alarm depends on the voting of the two classifiers, which brings the perspectives on different time scales for decision making. In the test, 96.14% of the threat vibration signals can be detected, while only 0.45% of the environmental noise signals cause false alarms.

Keywords

Acknowledgement

The authors would like to acknowledge the National Natural Science Foundation of China (Grant No. 51878509) and the State Key Laboratory of Disaster Reduction in Civil Engineering (Project: SLDRCE19-B-25) for the financial support to perform the work in this project.

References

  1. Banu, V.C., Costea, I.M., Nemtanu, F.C. and Badescu, I. (2017), "Intelligent video surveillance system", Proceedings of the 23rd International Symposium for Design and Technology in Electronic Packaging, Constanta, Romania, October.
  2. Bock, Y., Prawirodirdjo, L. and Melbourne, T.I. (2004), "Detection of arbitrarily large dynamic ground motions with a dense high-rate GPS network", Geophys. Res. Lett., 31(6). https://doi.org/10.1029/2003GL019150
  3. Bock, Y., Melgar, D. and Crowell, B.W. (2011), "Real-time strong-motion broadband displacements from collocated GPS and accelerometers", Bull. Seismol. Soc. Am., 101(6), 2904-2925. https://doi.org/10.1785/0120110007
  4. Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  5. EGIG (2018), "10th report of the European gas pipeline incident data group", VA 17.R.0395; European Gas Pipeline Incident Data Group, Europe.
  6. Fahmy, H.M.A. (2016), Wireless Sensor Networks, Springer, Singapore.
  7. Fernandez, J., Calavia, L., Baladron, C., Aguiar, J.M., Carro, B., Sanchez-Esguevillas, A., Alonso-Lopez, J.A. and Smilansky, Z. (2013), "An intelligent surveillance platform for large metropolitan areas with dense sensor deployment", Sensors, 13(6), 7414-7442. https://doi.org/10.3390/s130607414
  8. Ghatak, S., Bose, S. and Roy, S. (2014), "Intelligent wall mounted wireless fencing system using wireless sensor actuator network", Proceedings of the 2014 International Conference on Computer Communication and Informatics, Coimbatore, India, January.
  9. Hausamann, D., Zirnig, W., Schreier, G. and Strobl, P. (2005), "Monitoring of gas pipelines-a civil UAV application", Aircr. Eng. Aerosp. Technol., 77(5), 352-360. https://doi.org/10.1108/00022660510617077
  10. Jiang, F., Li, H., Zhang, Z. and Zhang, X. (2018), "An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN", Proceedings of the 2017 International Conference on Optical Instruments and Technology: Advanced Optical Sensors and Application, Beijing, China, January.
  11. Li, S., Guo, Y., Xu, Y. and Li, Z. (2019), "Real-time geometry identification of moving ships by computer vision techniques in bridge area", Smart Struct. Syst., Int. J., 23(4), 359-371. https://doi.org/10.12989/sss.2019.23.4.359
  12. Liu, P., Chen, A.Y., Huang, Y.N., Han, J.Y., Lai, J.S., Kang, S.C., Wu, T.H., Wen, M.C. and Tsai, M.H. (2014), "A review of rotorcraft unmanned aerial vehicle (UAV) developments and applications in civil engineering", Smart Struct. Syst., Int. J., 13(6), 1065-1094. https://doi.org/10.12989/sss.2014.13.6.1065
  13. Makhoul, N., Limongelli, M.P. and Jaoude, R.A. (2018), "Structural Health Monitoring of buried pipelines under seismic hazard: A reivew of damage scenarios and sensing techniques", Proceedings of the 16th European conference on Earthquake Engineering, Thessaloniki, Greece, June.
  14. Martinez, A.M. and Kak, A.C. (2001), "PCA versus LDA", IEEE Transact. Pattern Anal. Mach. Intell., 23(2), 228-233. https://doi.org/10.1109/34.908974
  15. Muhlbauer, W.K. (2004), Pipeline risk management manual: ideas, techniques, and resources, Third Edition, Elsevier, Netherlands.
  16. Okada, Y., Kasahara, K., Hori, S., Obara, K., Sekiguchi, S., Fujiwara, H. and Yamamoto, A. (2004), "Recent progress of seismic observation networks in Japan-Hi-net, F-net, K-NET and KiK-net", Earth Planets Space, 56(8), 15-28. https://doi.org/10.1186/BF03353076
  17. Peng, F., Wu, H., Jia, X.H., Rao, Y.J., Wang, Z.N. and Peng, Z.P. (2014), "Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines", Optics Express, 22(11), 13804-13810. https://doi.org/10.1364/OE.22.013804
  18. PHMSA (2017), "2017 Hazmat summary by mode of transportation/cause", Pipeline and Hazardous Materials Safety Administration, U.S. Department of Transportation, Washington, USA.
  19. Pradhan, B. (2013), "A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS", Comput. Geosci., 51, 350-365. https://doi.org/10.1016/j.cageo.2012.08.023
  20. Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M. and Sabeti, P.C. (2011), "Detecting novel associations in large data sets", Science, 334(6062), 1518-1524. https://doi.org/10.1126/science.1205438
  21. Sun, J. and Wen, J. (2013), "Target location method for pipeline pre-warning system based on HHT and time difference of arrival", Measurement, 46(8), 2716-2725. https://doi.org/10.1016/j.measurement.2013.04.059
  22. Sun, Q., Feng, H., Yan, X. and Zeng, Z. (2015), "Recognition of a phase-sensitivity OTDR sensing system based on morphologic feature extraction", Sensors, 15(7), 15179-15197. https://doi.org/10.3390/s150715179
  23. Tejedor, J., Maciasguarasa, J., Martins, H.F., Piote, D., Pastorgraells, J., Martinlopez, S., Corredera, P., Gonzalezherraez, M. (2017), "A novel fiber optic based surveillance system for prevention of pipeline integrity threats", Sensors, 17(2), 355. https://doi.org/10.3390/s17020355
  24. Tejedor, J., Macias-Guarasa, J., Martins, H.F., Pastor-Graells, J., Martin-Lopez, S., Guillen, P.C., De Pauw, G., De Smet, F., Postvoll, W., Ahlen, C.H. and Gonzalez-Herraez, M. (2018), "Real field deployment of a smart fiber-optic surveillance system for pipeline integrity threat detection: Architectural issues and blind field test results", J. Lightwave Technol., 36(4), 1052-1062. http://dx.doi.org/10.1109/JLT.2017.2780126
  25. Wan, C. and Mita, A. (2009), "Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale", Smart Mater. Struct., 18(5), 055004. http://dx.doi.org/10.1088/0964-1726/18/5/055004
  26. Wang, N., Fang, N. and Wang, L. (2019), "Intrusion recognition method based on echo state network for optical fiber perimeter security systems", Optics Communications, 451, 301-306. http://dx.doi.org/10.1016/j.optcom.2019.06.058
  27. Wu, C.F.J. (1986), "Bootstrap and other resampling methods in regression analysis", Annals Statist., 14(4), 1261-1295. http://dx.doi.org/10.1214/aos/1176350142
  28. Wu, H., Xiao, S., Li, X., Wang, Z., Xu, J. and Rao, Y. (2015), "Separation and determination of the disturbing signals in phase-sensitive optical time domain reflectometry (Φ-OTDR)", J. Lightwave Technol., 33(15), 3156-3162. http://dx.doi.org/10.1109/JLT.2015.2421953
  29. Zhang, Y. (2018), "Vibration Characteristic Analysis and Pattern Recognition of Third Party Instrusion in Buried Pipelines", M.D. Dissertation, Tongji University, Shanghai, China.
  30. Zhang, Y. and Li, S.Z. (2018), "Pipeline incident statistics from 2010 to 2017", Department of Structural Engineering, Tongji University, Shanghai, China.
  31. Zhang, Y.L., Zhang, Z.Q., Xiao, G., Wang, R.D. and He, X. (2015), "Perimeter intrusion detection based on intelligent video analysis", Proceedings of the 15th International Conference on Control, Automation and Systems (ICCAS), Busan, South Korea, October.
  32. Zheng, F., Zhang, G. and Song, Z. (2001), "Comparison of different implementations of MFCC", J. Comput. Sci. Technol., 16(6), 582-589. http://dx.doi.org/10.1007/BF02943243
  33. Zhu, Y., Lei, Z., Zheng, W., Ma, H., Xia, R. and Song, D. (2019), "Research on substation perimeter isolation based on phased array radar and multi-video fusion technology", Journal of Physics: Conference Series, 1187(2), 22-54. http://dx.doi.org/10.1088/1742-6596/1187/2/022054