A wireless impedance analyzer for automated tomographic mapping of a nanoengineered sensing skin

Sukhoon Pyo, Kenneth J. Loh, Tsung Chin Hou, Erik Jarva, Jerome P. Lynch

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

Polymeric thin-film assemblies whose bulk electrical conductivity and mechanical performance have been enhanced by single-walled carbon nanotubes are proposed for measuring strain and corrosion activity in metallic structural systems. Similar to the dermatological system found in animals, the proposed self-sensing thin-film assembly supports spatial strain and pH sensing via localized changes in electrical conductivity. Specifically, electrical impedance tomography (EIT) is used to create detailed mappings of film conductivity over its complete surface area using electrical measurements taken at the film boundary. While EIT is a powerful means of mapping the sensing skin's spatial response, it requires a data acquisition system capable of taking electrical impedance measurements on a large number of electrodes. A low-cost wireless impedance analyzer is proposed to fully automate EIT data acquisition. The key attribute of the device is a flexible sinusoidal waveform generator capable of generating regulated current signals with frequencies from near-DC to 20 MHz. Furthermore, a multiplexed sensing interface offers 32 addressable channels from which voltage measurements can be made. A wireless interface is included to eliminate the cumbersome wiring often required for data acquisition in a structure. The functionality of the wireless impedance analyzer is illustrated on an experimental setup with the system used for automated acquisition of electrical impedance measurements taken on the boundary of a bio-inspired sensing skin recently proposed for structural health monitoring.

Original languageEnglish
Pages (from-to)139-155
Number of pages17
JournalSmart Structures and Systems
Volume8
Issue number1
DOIs
Publication statusPublished - 2011 Jul

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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