Topology reconstruction of wiring networks using an iterative process based on Time-Domain Reflectometry and Forensic-Based Investigation algorithm
Source abstract
Abstract
Time-Domain Reflectometry (TDR) generally consists of injecting a signal into the Network Under Test (NUT), and then collecting the multiple reflections that occur from each instance of junction or termination. However, since reflectometers combine primary reflections with multiple and intermediate reflections, pulses in the reflectometry response often overlap. Therefore, reconstructing wiring networks using only TDR responses is not feasible. In this paper, the Wiring Network Reconstruction (WNR) process is formulated as an optimisation problem. The optimisation algorithm used in this paper to solve the formulated problem is the Forensic-Based Investigation (FBI) algorithm. Solving the WNR problem consists of finding the topology and the length of branches of the NUT. In the objective function, the optimisation algorithm compares the TDR obtained from the NUT with the TDR of the predicted solutions, where all TDRs are generated analytically to save time. The obtained results using the FBI algorithm are tested against ten well-known optimisation algorithms over a set of seven experiments. In these experiments, five cases are simulation-based, while the last two are real-world cases. The results provided in the paper clearly show the effectiveness and resilience of the proposed approach for reconstructing wiring networks with different degrees of complexity.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electrical Fault Detection and Protection · Integrated Circuits and Semiconductor Failure Analysis · Advanced Surface Polishing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University of Freres Mentouri Constantine1
University of Hafr Al Batin
Institut Polytechnique des Sciences Avancées (IPSA)
Université Paris-Saclay, Sorbonne Université
Umm Al-Qura University
University of L’Aquila
University of M’sila
University of Freres Mentouri Constantine 1
References (36)
Show source-provided references
1. 10.1016/j.measurement.2022.11137810.1016/j.measurement.2022.111378 ↗
2. 10.1109/JSEN.2020.298732110.1109/JSEN.2020.2987321 ↗
3. Boudjefdjouf H, Bouchekara HREH, Mehasni R, et al. Wire fault diagnosis using time-domain reflectometry and backtracking search optimization algorithm. Appl Comput Electromagn Soc J. 2016;31(4):340–347.
4. 10.1109/JSEN.2005.85896410.1109/JSEN.2005.858964 ↗
5. 10.1109/TEMC.2003.81130510.1109/TEMC.2003.811305 ↗
6. 10.1002/stc.6910.1002/stc.69 ↗
7. 10.1002/0471654507.eme12310.1002/0471654507.eme123 ↗
8. 10.1109/APS.2002.101696910.1109/APS.2002.1016969 ↗
9. 10.1109/TMTT.1965.112596410.1109/TMTT.1965.1125964 ↗
10. Pozar DM. Microwave engineering. 2017.
11. 10.1109/TMAG.2010.204372010.1109/TMAG.2010.2043720 ↗
12. 10.1016/j.compeleceng.2014.07.00210.1016/j.compeleceng.2014.07.002 ↗
13. 10.1080/10589759.2016.120057610.1080/10589759.2016.1200576 ↗
14. 10.1080/02726343.2013.75629110.1080/02726343.2013.756291 ↗
15. 10.1109/I2MTC.2018.840972510.1109/I2MTC.2018.8409725 ↗
16. 10.1109/TMAG.2010.208950310.1109/TMAG.2010.2089503 ↗
17. 10.1049/iet-smt.2019.012210.1049/iet-smt.2019.0122 ↗
18. 10.1109/COMPUMAG45669.2019.903281910.1109/COMPUMAG45669.2019.9032819 ↗
19. 10.1080/10589759.2024.230471010.1080/10589759.2024.2304710 ↗
20. Reflectometry T, Slimani H, Maurice F, et al. Machine learning approach for classification of faults in cable via compressed sensing. In: ‘2024 Prognostics and System Health Management Conference (PHM)’; Stockholm, Sweden. IEEE; 2024. p. 300–305.
21. 10.1016/j.engappai.2023.10728910.1016/j.engappai.2023.107289 ↗
22. Wang L, Chen H, Chen Y. Cable online fault diagnosis technology based on ZCZ spread spectrum sequence. In: ‘2024 Prognostics and System Health Management Conference (PHM)’; Stockholm, Sweden. IEEE; 2024. p. 287–292.
23. 10.1016/j.asoc.2020.10633910.1016/j.asoc.2020.106339 ↗
24. 10.1049/iet-smt.2016.042710.1049/iet-smt.2016.0427 ↗
25. 10.1109/6040.92875410.1109/6040.928754 ↗
26. 10.1109/15.97464710.1109/15.974647 ↗
27. Bouchekara HREH. Electric charged particles optimization and its application to the optimal design of a circular antenna array. Netherlands: Springer; 2020.
28. 10.1016/j.engappai.2013.09.01310.1016/j.engappai.2013.09.013 ↗
29. 10.1109/MHS.1995.49421510.1109/MHS.1995.494215 ↗
30. 10.1049/iet-smt.2018.519410.1049/iet-smt.2018.5194 ↗
31. Karaboga D. An idea based on honey bee swarm for numerical optimization. Tech Rep TR06, Erciyes Univ. 2005;TR06:10.
32. 10.1023/A:102245262630510.1023/A:1022452626305 ↗
33. 10.1023/A:100820282132810.1023/A:1008202821328 ↗
34. 10.1109/CEC48606.2020.918557710.1109/CEC48606.2020.9185577 ↗
35. 10.1109/TEVC.2009.201461310.1109/TEVC.2009.2014613 ↗
36. 10.1109/CEC.2013.655755510.1109/CEC.2013.6557555 ↗