CBO-Based TDR Approach for Wiring Network Diagnosis

• Published in: IET science, measurement & technology Vol. 10; no. 2; pp. 329 - 348
• Main Authors: Boudjefdjouf, Hamza, de Paulis, Francesco, Bouchekara, Houssem, Orlandi, Antonio, Smail, Mostafa K.
• Format: Book Chapter Journal Article
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing; Institution of Engineering and Technology
• Series: Modeling and Optimization in Science and Technologies
• Subjects: 3D graphics & modelling; Artificial intelligence; Colliding bodies optimization; Diagnosis of wiring network; Electromagnetism; Engineering Sciences; Fault detection; Inverse problem; Optimization; Time-domain reflectometry
• ISBN: 9783319509198; 3319509195
• ISSN: 2196-7326; 1751-8822; 2196-7334; 1751-8830
• DOI: 10.1007/978-3-319-50920-4_13

Wiring networks are vital connections in which power and signals can be transmitted. Defects in these networks can have dramatic consequences, and it is therefore of paramount importance to quickly detect and accurately locate and characterize defects in these networks. In one side, the time-domain reflectometry (TDR) is a measurement concept that exploits reflected waveforms in order to identify the characteristics of wiring networks. In the other side, the colliding bodies optimization (CBO) algorithm has proven to be efficient and robust for solving optimization problems. The aim of this chapter was to combine both TDR and CBO in one approach for the diagnosis of wiring networks (DWN). In this approach, the DWN is formulated as an optimization problem, where the aim was to minimize the difference between the measured TDR response (of the network under test) and a generated one in order to get information about the status of this network. The proposed approach is validated using six experiments with two different configurations of wiring networks. The results presented in this chapter show that the proposed approach can be used for a reliable DWN.