Wiring Network Diagnosis Using Reflectometry and Twin Support Vector Machines

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.