Experimental Validation of the Inverse Scattering Method for Distributed Characteristic Impedance Estimation

Abstract

Recently published theoretic results and numerical simulations have shown the ability of inverse scattering-based methods to diagnose soft faults in electric cables, in particular, faults implying smooth spatial variations of cable characteristic parameters. The purpose of the present paper is to report laboratory experiments confirming the ability of the inverse scattering method for retrieving spatially distributed characteristic impedance from reflectometry measurements. Various smooth or stepped spatial variations of characteristic impedance profiles are tested. The tested electric cables are CAN unshielded twisted pairs used in trucks and coaxial cables.Index Terms-Cable health monitoring, frequency domain reflectometry, inverse scattering, soft fault diagnosis.

A New Insight on the “S” Shape Pattern of Soft Faults in Time-Domain Reflectometry

“…Equation (6) clearly emphasizes that the time-domain impulse response to a soft defect on an electrical line consists of the superposition of multiple echoes at each one of its interfaces.…”

“…During the past decade, time-domain reflectometry (TDR) has proven to be an effective tool for the detection and localization of defects on an electrical line [ 3 , 4 , 5 ]. Hard faults are easily detected, but soft fault detection remains a difficult task since their associated TDR signature is weak [ 6 ]. Visual inspection is time-consuming and the cost is elevated, especially if there is no clue about the existence of a defect and its whereabouts.…”

Enhancing Impedance Estimation Of Microstrip Transmission Lines Using Neural Network Models With Frequency Domain Reflectometry

“…1) Estimation of Imaginary Part of Impedance: While previous studies [3], [4], [15], [25], [26] have focused on approximating the real component of the impedance, our proposed NN models are trained to estimate both the real and imaginary components of the impedance. 2) Computational Cost & Non-linearity: Unlike iterative optimization methods presented in the literature [3]- [6], [8]- [20], [25], our methods estimate the impedance of the TL in a single step after the training phase.…”

Dynamic System Fault Diagnosis Under Sparseness Assumption

“…A large number of possible faults is typically encountered with distributed parameter systems. For example, in mechanical structure health monitoring [13], in electrical cable monitoring [14] and in petrochemical infrastructure monitoring [15].…”