Vol. 100
Latest Volume
All Volumes
PIERC 150 [2024] PIERC 149 [2024] PIERC 148 [2024] PIERC 147 [2024] PIERC 146 [2024] PIERC 145 [2024] PIERC 144 [2024] PIERC 143 [2024] PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2020-02-21
Distributed Sensor Diagnosis in Twisted Pair Networks for Soft Fault Identification Using Reflectometry and Neural Network
By
Progress In Electromagnetics Research C, Vol. 100, 83-93, 2020
Abstract
This paper aims at developing an approach allowing to detect, locate and characterize soft faults (i.e. isolation damage) in branched network composed of shielded twisted pair (STP) cables. To do so, a distributed reflectometry diagnosis where several sensors (reflectometers) are placed at different ends of the network is used to maximize the diagnosis coverage. The soft fault identification is achieved by using the Multi-Carrier Time Domain Reflectometry (MCTDR) combined with a Multi-Layer Perceptron Neural Network (MLP-NN). The main novelty here lies in the fact that the MLP-NN method is used for data fusion from several distributed reflectometers, which would eliminate ambiguities related to the fault location. The required datasets for training and testing of the NN are generated by simulation. Simulation and experimental results are dedicated to the validation of the proposed approach for locating and characterizing the soft faults in branched networks.
Citation
Ousama Osman, Soumaya Sallem, Laurent Sommervogel, Marc Olivas Carrion, Pierre Bonnet, and Françoise Paladian, "Distributed Sensor Diagnosis in Twisted Pair Networks for Soft Fault Identification Using Reflectometry and Neural Network," Progress In Electromagnetics Research C, Vol. 100, 83-93, 2020.
doi:10.2528/PIERC19122402
References

1. Griffiths, L. A., R. Parakh, C. Furse, and B. Baker, "The invisible fray: A critical analysis of the use of reflectometry for fray location," IEEE Sensors Journal, Vol. 6, No. 3, 697-706, 2006.
doi:10.1109/JSEN.2006.874017

2. 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, No. 5, 1654-1663, May 2011.
doi:10.1109/TIM.2011.2105030

3. Shin, Y. J., et al., "Application of time-frequency domain reflectometry for detection and localization of a fault on a coaxial cable," IEEE Trans. Instrum. Meas., Vol. 54, No. 6, 2493-2500, Dec. 2005.
doi:10.1109/TIM.2005.858115

4. Shi, Q. and O. Kanoun, "Wire fault diagnosis in the frequency domain by impedance spectroscopy," IEEE Trans. Instrum. Meas., Vol. 64, No. 8, 2179-2187, Aug. 2015.
doi:10.1109/TIM.2014.2386918

5. Lelong, A., M. Olivas Carrion, V. Degardin, and M. Lienard, "On line wiring diagnosis by modified spread spectrum time domain reflectometry," PIERS Proceeding, 182-186, Hangzhou, China, Mar. 24–28, 2008.

6. Auzanneau, F., "Wire troubleshooting and diagnosis: Review and perspectives," Progress In Electromagnetics Research B, Vol. 49, 253-279, 2013.
doi:10.2528/PIERB13020115

7. Osman, O., S. Sallem, L. Sommervogel, M. Olivas Carrion, A. Peltier, P. Bonnet, and F. Paladian, "Method to improve fault location accuracy against cables dispersion effect," Progress In Electromagnetics Research Letters, Vol. 83, 29-35, 2019.
doi:10.2528/PIERL19021907

8. Ben Hassen, W., F. Auzanneau, L. Incarbone, F. Peres, A. Francois, "Distributed sensor fusion for wire fault location using sensor clustering strategy," International Journal of Distributed Sensors Networks, 1-17, 2015, ISSN 1550-1329.

9. Lelong, A., L. Sommervogel, N. Ravot, and M. Olivas Carrion, "Distributed reflectometry method for wire fault location using selective average," IEEE Sensors Journal, Vol. 10, No. 2, 300-310, Feb. 2010.
doi:10.1109/JSEN.2009.2033946

10. El Sahmarany, L., L. Berry, N. Ravot, F. Auzanneau, and P. Bonnet, "Time reversal for soft faults diagnosis in wire networks," Progress In Electromagnetics Research M, Vol. 31, 45-58, 2013.
doi:10.2528/PIERM13032801

11. Kafal, M., R. Razzaghi, A. Cozza, F. Auzanneau, and W. Ben-Hassen, "A review on the application of the time reversal theory to wire network and power system diagnosis," IEEE International Instrumentation and Measurement Technology Conference, Auckland, New Zealand, May 2019.

12. Abboud, L., A. Cozza, and L. Pichon, "A non-iterative method for locating soft faults in complex wire networks," IEEE Transactions on Vehicular Technology, Vol. 62, No. 3, 1010-1019, 2013.
doi:10.1109/TVT.2013.2237796

13. Kafal, M., A. Cozza, and L. Pichon, "Locating multiple soft faults in wire networks using alternative DORT implementation," IEEE Transactions on Instrumentation and Measurements, Vol. 65, No. 2, 399-406, 2015.
doi:10.1109/TIM.2015.2498559

14. Lelong, A. and M. Carrion, "On line wire diagnosis using multi-carrier time domain reflectometry for fault location," 2009 IEEE Sensors, 751-754, Oct. 2009.

15. Sallem, S. and O. Osman, "Wired network distributed diagnosis and sensors communications by Multi-carrier Time Domain reflectometry," IEEE Intelligent Systems Conference, London, UK, Sep. 2018.

16. Amloune, A., H. Bouchekara, M. K. Smail, F. de Paulis, et al. "An intelligent wire fault diagnosis approach using time domain reflectometry and pattern recognition network," Nondestructive Testing and Evaluation, 2018, DOI: 10.1080/10589759.2018.1559312.

17. Laib, A., M. Melit, B. Nekoul, K. E. K. Drissi, and K. Kerroum, "Soft fault identification in electrical network using time domain reflectometry and neural network," LNEE Lecture Notes in Electrical Engineering, 365-376, Springer, Jan. 2019.
doi:10.1007/978-3-319-97816-1_28

18. Smail, M. K., T. Hacib, L. Pichon, and F. Loete, "Detection and location of defects in wiring networks using time domain reflectometry and neural network," IEEE Trans. on Magn., Vol. 47, No. 5, 1502-1505, May 2011.
doi:10.1109/TMAG.2010.2089503

19. Osman, O., S. Sallem, L. Sommervogel, M. Olivas, et al. "Distributed sensor diagnosis in complex wired networks for soft fault detection using reflectometry and neural network," IEEE Autotestcon, USA, Aug. 2019.

20. Tang, H. and Q. Zhang, "An inverse scattering approach to soft fault diagnosis in lossy electric transmission lines," IEEE Transactions on Antennas and Propagation, Vol. 59, No. 10, 3730-3737, Nov. 2011.
doi:10.1109/TAP.2011.2163772

21. Hayt, W., Engineering Electromagnetics, 6th Ed., 437-440, McGraw-Hill, 1989.

22. Zhang, J., Q. B. Chen, Z. Qiu, J. L. Drewniak, and A. Orlandi, "Extraction of causal RLGC models from measurements for signal link path analysis," 2008 International Symposium on Electromagnetic Compatibility — EMC Europe, 1-6, Hamburg, 2008.

23. Ravot, N. and F. Auzanneau, "Defects detection and localization in complex topology wired networks," Ann. Telecommun., Vol. 62, No. 1–2, 193-213, Jan. 2007.

24. Coccorse, E., R. Martone, and F. C. Morabit, "A neural network approach for the solution of electric and magnetic inverse problems," IEEE Trans. Magn., Vol. 30, No. 5, 2829-2839, Sep. 1994.
doi:10.1109/20.312527

25. Travassos, L., D. A. G. Vieira, N. Ida, C. Vollaire, and A. Nicolas, "Characterization of inclusions in a nonhomogenous GPR problem by artificial neural networks," IEEE Trans. Magn., Vol. 44, No. 6, 163-1633, Jun. 2008.

26. Zhou, Y., J. Hahn, and M. S. Mannan, "Fault detection and classification in chemical processes based on neural networks with feature extraction," ISA Transactions, Vol. 42, 651-664, 2003.
doi:10.1016/S0019-0578(07)60013-5

27. Lingling, M., F. Xu, et al. "Earthquake prediction based on levenberg-marquardt algorithm constrained back-propagation neural network using DEMETER data," Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management, 591-596, Belfast, Northern Ireland, UK, Sep. 2010.