Reliable Structural Failure Detection in Eye Bolts using Reflectometry Signals

Abstract

Eye bolts are critical elements of the electrical power distribution systems and structural failures on such devices can lead to service interruption, financial losses and hazard to civilians. Due to their installation characteristics, their common maintenance routine is costly, time-consuming and ineffective, because it depends on the de-energization of the circuit, disassembly and visual inspection of the bolts. In this paper, a new approach for detecting structural failures on eye bolts is proposed. An intelligent system based on an artificial neural network is used to process the reflectometry signals measured in order to detect the condition of the eye bolt automatically. The high accuracy in the experimental results suggests that the method proposed can improve the efficiency of the preventive maintenance routine performed on eye bolts, and, consequently, increase the reliability of the power distribution systems.