Truly Distributed Coaxial Cable Sensing based on Random Inhomogeneities

Abstract

Rayleigh backscattering-based distributed fiber optic sensing technology is well known and widely used for large-scale structural health monitoring. Inspired by the Rayleigh backscattering-based sensing methodology on an optical fiber, in this paper, we present a sensing concept based on the random inhomogeneities on a coaxial cable. As an analogy of Rayleigh backscattering along an optical fiber length, 'backscattering' also exists from a commercial coaxial cable due to its inherent defects along a cable length which induce a local variation (i.e., impedance mismatch). This is because of the irregular microscopic structures of the inner/outer conductors, and the inhomogeneous density or permittivity of the inner dielectrics after the cables are manufactured. The accumulated backscattered signals along the coaxial cable can be obtained using frequency-domain reflectometry. By analyzing the shift in the local backscattered signal, the local environmental perturbations (e.g., local strain or temperature) can be determined, so that truly distributed sensing capability using a coaxial cable can be achieved. To verify the proposed concept, an intact and commercial coaxial cable was demonstrated for distributed temperature sensing. Compared with the existing coaxial cable-based distributed sensing technologies, the proposed sensing concept does not need extra modifications to the coaxial cable and offers a truly distributed sensing capability.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Coaxial Cable; Distributed Sensing; Random Inhomogeneities; Rayleigh Backscattering; Temperature Sensing

International Standard Serial Number (ISSN)

0018-9456; 1557-9662

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

15 Jan 2019

Link to Full Text

Share

 
COinS