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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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., 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.
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.
19. Osman, O., 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.
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.
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.
27. Lingling, M., 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.