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Structural faults on anchor rods due to corrosive processes are among the major natural causes worldwide for the collapse of guyed towers. As such towers are widely used on both power transmission lines and antenna systems, their collapse may cause unexpected interruptions of telecommunications and power delivery services. Despite being of utmost importance, underground corrosion monitoring is still a technological challenge, because it is a highly nonlinear problem, influenced by several soil characteristics and its complex iterations with the metallic structure of the rods. Some techniques have already been proposed to locate such faults; however, either they require previous knowledge about the medium or they need the interpretation of the acquired signal by a human specialist. These requirements make it difficult to use such solutions in automatic monitoring as well as to make their integration with Internet of Things (IoT) systems. In this article, a novel technique is proposed to automatically locate structural faults on anchor rods, based on a two-step machine learning (ML) solution. The system is fed by a vector composed of frequency-domain samples of the input impedance electromagnetic (EM) signals measured on the rods through a dedicated designed connector. Experimental results have demonstrated that the proposed combination of EM signal acquisition and ML data processing was able to perform the fault location on anchor rods accurately and with a total independence of any additional human analysis.

Detecting faults on the anchor rods of power transmission line towers is an essential procedure for guaranteeing the safety, operability, and availability of the electric power system. Such a measure aims to minimize the risk of tower collapse and the associated costs due to the lack of energy, injuries to people, and damage to the reputation of the companies involved. In this letter, we report an alternative noninvasive and nondestructive detection technique through the acquisition of electromagnetic scattering parameters. A novel machine learning (ML) approach is proposed in order to achieve the advantages of such a technique and to deal with the respective drawbacks due to the complexity of signals acquired. A strategy to artificially increase the number of relevant samples in the dataset through the simulations of computational models is performed. Different ML classification algorithms are compared in the task of detecting structural faults in anchor rods in order to define the most suitable to this application. Finally, experimental results show that the methodology proposed achieves a high detection accuracy, surpassing other known methods.

This paper introduces a new strategy to determine the soil permittivity and its loss tangent which uses simulation and measurement result for comparison. The setup of the measurement system includes a high frequency connector and a two-rod transmission line (TRTL) where one of the rods is embedded in an unknown-permittivity soil. In order to validate the simulation model by optimizing the complex permittivity of the soil from the measurement result a smart searching tool performed by the PSO (Particle Swarm Optimization) algorithm is applied. The measurements were carried out by a VNA (Vector Network Analyzer) to obtain the reflected signals from the TRTL for a non-parameterized soil. The effectiveness of the PSO algorithm is demonstrated by equalizing both simulated and measurement results of the soil complex permittivity in the simulation model.

Metallic anchor rods are used to fix transmission tower guy wires. In order to perform visual inspections on the integrity of the anchor rods, electricity companies are obliged to undertake soil excavation, which is both costly and hazardous. Despite this, Frequency domain reflectometry (FDR) may be used to detect faults on anchor rods, and is enabled by a robust, high-frequency microwave device for support and connection. Impedance changes occur along the rod due to faults, thereby causing reflections, which are detected during FDR measurement analysis. However, auxiliary structures on the anchorage system also provide similar results during the analysis, and may therefore be confused with faults. An innovative, new approach is proposed in order to minimize the effects of small structures positioned at a distance from the fault, so that the fault reflection remains the same. Preliminary results have indicated reductions of up to 95% of the undesired reflection values.

Network parameters are capable of conveying information about the constitution of a medium in which a highfrequency wave propagates. In this paper, this characteristic is exploited in order to design a nondestructive system to detect corrosion in anchor rods of guyed towers. A compound database is built with the simulated and measured parameters return loss, input impedance, and voltage standing wave ratio for normal and faulty rods examples. Artificial neural networks are used to capture underlying characteristics of data and establish relationships between these parameters and the presence of corrosion in the rods, without the need for physical models. Experimental results show that the innovative use of a multiparameter strategy achieves a high accuracy and enhances the detection capacity of the system.

This paper presents a new methodology based on a FDR technique and machine learning structure to detect faults on anchor rods used in guyed towers of power transmission lines. Such faults are due to material loss in the rods, which it may break them, and may case the tower falling. The methodology is a non-destructive technique (NDT) that is based on frequency domain reflectometry (FDR). For detection, high frequency pulses are inserted into anchor rod samples and the reflection coefficient is analyzed and compared to that obtained in the simulation results. Samples of anchor rods with and without faults were simulated, fabricated, measured and classified by a machine learning structure to determine the presence or absence of the faults.

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.

Eye bolts are part of a system used to anchor electrical insulators in substations and on transmission lines, connected by concrete structures. Structural failures in these devices may lead to service interruption, financial losses and risks for the general population. In this article, we propose a signal adapter capable of applying and receiving high frequency signals to a portable vector network analyzer for the eye bolt. The technique consists of injecting high frequency signals into the samples in order to detect their integrity and analyzing their return loss (S11), using the principle of reflection and transmission lines. Should the eye bolt present a failure, the ∣S11∣ response is changed, thereby enabling its detection. The development of the adapter, for both intact and defective bolt models, was based on electromagnetic simulation models using the Ansys HFSS program. The measurements were made with models of real bolts presenting approximate wear, in order to validate the simulations.