volume | PIER Journals

Abstract

Based on Time-Domain Reflectometry (TDR) technique, a novel method which could locate faults on the coaxial cable distribution network by using Support Vector Machine (SVM) is proposed in this paper. This approach allows the faulty network to be reconstructed by estimating the lengths of branches. A State-transition Matrix model is employed to simulate the TDR response at any port and evaluate the transfer function between two points. SVM is used to solve the inversion problem through training datasets created by the State-transition matrix model. Compared to the existing reflectometry methods, our proposed method can tackle multiple faults in the complex cable networks. Numerical and experimental results pointing out the performance of the SVM model in locating faults are reported.

1. Boyd, E., H. Elbakoury, M. Hajduczenia, and A. Liu, "EPON over Coax (EPoC)," IEEE Commun. Mag., Vol. 50, No. 9, 88-95, 2012.
doi:10.1109/MCOM.2012.6295717

2. IEEE 802.3 Working Group, 2012, http://www.ieee802.org/3/ep-oc.

3. Lelong, A., L. Sommervogel, N. Ravot, and M. Carrion, "Distributed reflectometry method for wire fault location using selective average," IEEE Sens. J., Vol. 2, 300-310, 2010.
doi:10.1109/JSEN.2009.2033946

4. Cataldo, A., G. Cannazza, E. De Benedetto, and N. Giaquinto, "Experimental validation of a TDR-based system for measuring leak distances in buried metal pipes," Progress In Electromagnetics Research, Vol. 132, 71-90, 2012.

5. Kwak, K. S., T. Choe, J. Park, and T. Yoon, "Application of time-frequency domain reflectometry for measuring load impedance," IEICE Electronics Express, Vol. 5, 107-113, 2008.
doi:10.1587/elex.5.107

6. Schuet, S., D. Timucin, and K. Wheeler, "A model-based probabilistic inversion framework for characterizing wire fault detection using TDR," IEEE Trans. Instrum. Meas., Vol. 60, 1654-1663, 2011.
doi:10.1109/TIM.2011.2105030

7. Pourahmadi-Nakhli, M. and A. A. Safavi, "Path characteristic frequency-based fault locating in radial distribution systems using wavelets and neural networks ," IEEE Trans. Power Del., Vol. 26, 772-781, 2011.
doi:10.1109/TPWRD.2010.2050218

8. Vakula, D. and N. V. S. N. Sarma, "Using neural networks for fault detection in planar antenna arrays," Progress In Electromagnetics Research Letters, Vol. 14, 21-30, 2010.
doi:10.2528/PIERL10030401

9. Meng, J., Y. Gao, and Y. Shi, "Support vector regression model for measuring the permittivity of asphalt concrete," IEEE Microw. Wirel. Co., Vol. 17, No. 12, 2007.
doi:10.1109/LMWC.2007.910462

10. Zhang, Y. and L. Wu, "An Mr brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.

11. Thukaram, D., H. P. Khincha, and H. P. Vijaynarasimha, "Artificial neural network and support vector machine approach for locating faults in radial distribution systems ," IEEE Trans. Power Del., Vol. 20, No. 2, 710-721, 2005.
doi:10.1109/TPWRD.2005.844307

12. Angiulli, G., D. De Carlo, G. Amendola, E. Arnieri, and S. Costanzo, "Support vector regression machines to evaluate resonant frequency of elliptic substrate integrate waveguide resonators," Progress In Electromagnetics Research, Vol. 83, 107-118, 2008.
doi:10.2528/PIER08041803

13. Ni, J., L. Ren, C. Zhang, and S. Yang, "Abrupt event monitoring for water environment system based on KPCA and SVM," IEEE Trans. Instrum. Meas., Vol. 61, 980-989, 2012.
doi:10.1109/TIM.2011.2173000

14. Wu, Y., Z. X. Tang, B. Zhang, and Y. Xu, "Permeability measurement of ferromagnetic materials in microwave frequency range using support vector machine regression," Progress In Electromagnetics Research, Vol. 70, 247-256, 2007.
doi:10.2528/PIER07012801

15. Chen, W. Y. and K. Kerpez, "Coaxial cable distribution plant performance simulation for interactive multimedia TV," Global Telecommunications Conference, 173-177, 1995.

16. Zimmermann, M. and K. Dostert, "A multipath model for the powerline channel," IEEE Trans. Commun., Vol. 50, 553-559, 2002.
doi:10.1109/26.996069

17. Cristianini, N. and J. S. Taylor, An Introduction to Support Vector Machines, Cambridge University Press, London, 2000.

18. Tan, C. P., J. Y. Koay, K. S. Lim, H. T. Ewe, and H.-T. Chuah, "Classification of multi-temporal SAR images for rice crops using combined entropy decomposition and support vector machine technique," Progress In Electromagnetics Research, Vol. 71, 19-39, 2007.
doi:10.2528/PIER07012903

19. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM based-estimators for inverse scattering problems ," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801

20. Bottou, L., O. Chapelle, D. DeCoste, and J. Weston, Large Scale Kernel Machines, MIT Press, Cambridge, MA, 2007.

21. Cawley, G. C., "Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs," International Joint Conference on Neural Networks, IJCNN, 1661-1668, 2006.

22. Kowalski, M., "A simple and efficient computational approach to chafed cable time-domain reflectometry signature prediction," Proc. Annu. Rev. Progress ACES Conf., 2009.

Dr. Xiaolong Zhang

Dr. Minming Zhang

Dr. Deming Liu