Analysis of TDR Signals with Convolutional Neural Networks
Marco Scarpetta, Maurizio Spadavecchia, G. Andria, Mattia Alessandro Ragolia, Nicola Giaquinto
Abstract
In this paper, a method to estimate the position and the entity of capacitive faults in coaxial cables based on the time domain reflectometry (TDR) is presented. A convolutional neural network (CNN) is used to analyze the reflectometric signals obtained from transmission lines containing multiple capacitive faults. The great quantity of data necessary for training the neural network was generated using a transmission line simulator. After the training procedure, the CNN was tested on both simulated and measured signals. The testing results prove that the neural network is capable to produce good estimates of the line characteristics, even when working with complex reflectometric signals.