Design of a connector for fault detection in eye bolts used as insulator anchorages

Abstract

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.

Research on Non-destructive Testing Technology for Anchor Bolt Anchoring Quality Based on Virtual Instruments

“…In addition, it should be ensured that the software has the capability of real-time monitoring and remote control to meet the needs of engineering management. In the design and implementation process of the entire system, it is necessary to fully consider the collaborative work between hardware and software to achieve comprehensive monitoring and management of anchor bolt anchoring quality [3].…”

Detection of Eyebolt Faults Using a Random Forest Ensemble Model Based on Multiple High-Frequency Electromagnetic Parameters

“…In [19] and [20] techniques are presented to measure microwave signals of eye bolts. In [20] he presented an approach aimed at the design of the eyelet adapter and the study of the sensitivity of this adapter for six types of samples applied to a simplified system.…”

“…Concrete was not used as a dielectric medium, but air, and did not present any system classification. In [19], a more complete approach was presented than the one presented in [20] for detecting structural failures in eye bolts, a system was proposed considering the presence of concrete as a dielectric medium and an intelligent system based on an artificial neural network to process the measured reflectometry signals for the presence or absence of wear on eyebolts.…”

“…Based on [4], [5] and [19], this work presents a more accurate solution to the problem of detecting structural failures in anchoring eyes for power transmission lines. Because the random forest algorithm was used, which offers a greater capacity for data interpretation, is less susceptible to overfitting and outliers compared to the Artificial Neural Networks presented in [19], according to [4].…”

“…Based on [4], [5] and [19], this work presents a more accurate solution to the problem of detecting structural failures in anchoring eyes for power transmission lines. Because the random forest algorithm was used, which offers a greater capacity for data interpretation, is less susceptible to overfitting and outliers compared to the Artificial Neural Networks presented in [19], according to [4]. This article also proposes a method to evaluate the performance of a machine learning model using the k-fold crossvalidation technique [4] and in addition to accuracy, precision, recall and f1-score were analyzed to measure the performance of the system.…”