Artificial Neural Network-Based System for Location of Structural Faults on Anchor Rods Using Input Impedance Response

Abstract

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.