Wiring Network Diagnosis Using Reflectometry and Twin Support Vector Machines

Abdelhak Goudjil () and Mostafa Kamel Smail
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Abdelhak Goudjil: Aerospace Systems Department, Institut Polytechnique des Sciences Avancees (IPSA), 63, Boulevard de Brandebourg, 94200 Ivry-sur-Seine, France
Mostafa Kamel Smail: Aerospace Systems Department, Institut Polytechnique des Sciences Avancees (IPSA), 63, Boulevard de Brandebourg, 94200 Ivry-sur-Seine, France

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: The identification and resolution of faults, along with the proactive maintenance of wiring networks, are essential for ensuring the reliable, safe, and energy-efficient operation of industrial systems. Research in this domain advances fault detection and prevention, thereby enhancing overall safety, reliability, efficiency, and cost-effectiveness. Time-domain reflectometry (TDR) responses are extensively utilized for this purpose; however, their inherent nonlinearity and complexity pose significant challenges in interpretation. We propose an innovative solution to this problem that is aimed at diagnosing the state of the wiring network: integrating TDR responses with twin support vector machines (TWSVMs) by utilizing kernel functions. The effectiveness and feasibility of the TDR and TWSVM-based fault diagnosis methodology are substantiated through its application to two prevalent wiring network configurations, demonstrating superior performance compared to other fault diagnosis techniques.

Keywords: wiring network diagnosis; time-domain reflectometry; twin support vector machines; kernel functions; fault detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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