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Journal of Sensors
Volume 2017 (2017), Article ID 8950518, 11 pages
https://doi.org/10.1155/2017/8950518
Research Article

Impedance Based Health Monitoring Technique with Probabilistic Neural Network for Possible Wall Thinning Detection of Metal Structures

1Future Strategy & Convergence Research Institute, Korea Institute of Civil Engineering & Building Technology, Gyeonggi-do 10223, Republic of Korea
2Highway & Transportation Research Institute, Korea Institute of Civil Engineering & Building Technology, Gyeonggi-do 10223, Republic of Korea

Correspondence should be addressed to Wongi S. Na; moc.revan@48ignow

Received 18 July 2017; Accepted 27 August 2017; Published 9 October 2017

Academic Editor: Young-Jin Cha

Copyright © 2017 Wongi S. Na and Jongdae Baek. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Corrosion of structures and wall thinning of pipes can severely affect the mechanical strength as wall thickness is reduced. Thus a cost effective structural health monitoring technique plays an important role when managing a structure. The electromechanical impedance (EMI) method is a local method that has limited sensing range, resulting in a high cost when covering large areas. In this study, a reattachable EMI method is investigated using a stack of multiple metal plates to conduct an experiment involving thickness reduction. In addition, the main problem of the impedance signatures changing subjected to reattaching the piezoelectric transducer is solved by using the probabilistic neural network algorithm presented for the study. The proposed approach successfully identifies the thickness of two different structures with high accuracy.