This paper presents a field application system for detecting structural faults on anchor rods of cable-stayed towers of power transmission lines, based on a nondestructive technique using frequency domain reflectometry analysis. A specific high frequency connector was designed to interface a portable vector network analyzer and the buried rods in the test field. The soil electric permittivity was modeled using a full-wave electromagnetic simulation software and measurements. A machine learning structure was developed for the measured S11-parameter signals from the distinct buried rods to binary classify them as normal or faulty. An accuracy greater than 98% was achieved by characterizing the system as reliable and feasible for a novel predictive maintenance process for ground-anchored metallic rods.

本文提出了一种基于频域反射分析的无损检测技术，用于输电线路斜拉塔锚杆结构故障的现场应用系统。设计了一种特殊的高频连接器，以连接便携式矢量网络分析仪和测试现场中的埋入式测杆。使用全波电磁仿真软件和测量值对土壤电容率进行建模。开发了一种机器学习结构，用于测量来自不同掩埋杆的S11参数信号，以将其二进制分类为正常或故障。通过将系统描述为对地面锚定的金属棒进行新型预测性维护过程的可靠且可行的系统，可以达到98％以上的精度。