Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms
The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled u...
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| Published in | Computers & electrical engineering Vol. 40; no. 7; pp. 2236 - 2245 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2014
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0045-7906 1879-0755 |
| DOI | 10.1016/j.compeleceng.2014.07.002 |
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| Abstract | The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements. |
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| AbstractList | The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements. |
| Author | Smail, M.K. Mehasni, R. Bouchekara, H.R.E.H. Pichon, L. Boudjefdjouf, H. |
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| Cites_doi | 10.1016/j.compeleceng.2012.12.021 10.4236/ica.2011.24050 10.1016/j.compeleceng.2012.06.016 10.1109/TAP.2004.823969 10.1109/ICNN.1995.488968 10.1109/TMAG.2010.2089503 10.1109/ISSSE.1995.497999 10.5019/j.ijcir.2008.133 10.1016/j.compeleceng.2012.03.013 10.1109/61.311215 10.1016/j.compeleceng.2008.04.006 10.12989/sss.2006.2.1.025 10.1109/JSEN.2005.858964 10.1109/TEMC.2003.811305 |
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| Keywords | Wiring diagnosis Time Domain Reflectometry Finite Difference Time Domain method Particle Swarm Optimization Genetic Algorithm |
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| SubjectTerms | Computer simulation Diagnosis Engineering Sciences Finite Difference Time Domain method Genetic Algorithm Genetic algorithms Mathematical models Networks Particle Swarm Optimization Reflectometry Swarm intelligence Time Domain Reflectometry Wiring Wiring diagnosis |
| Title | Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms |
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