Damage detection in a steel frame using compressed EMI signatures based on compression sensing theory

DOI

10.2991/asei-15.2015.67How to use a DOI?

Keywords

Compression sensing; Electro-mechanical impedance (EMI); Steel Frame; Principal component analysis (PCA); Artificial neural network (ANN); Damage detection.

Abstract

An experiment was implemented using the electro-mechanical impedance (EMI) health monitoring technology for a framed structure and the conductance data were accordingly obtained for various damage severities. The compressed data by compression sensing theory were used to character the structural damages instead of the raw ones, and were further studied based on the principal component analysis (PCA). The obtained principal components were then employed to be the input parameters of the BP artificial neural network (ANN). Results showed that the transmission bandwidth and storage space of the EMI data were only 40% of the original ones using the present method. The neural network could identify the appearance of damages and could further classify the damage severities quantitatively using the principal components of the compressed conductance.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ji Zheng
AU  - Wei Yan
PY  - 2015/05
DA  - 2015/05
TI  - Damage detection in a steel frame using compressed EMI signatures based on compression sensing theory
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 313
EP  - 317
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.67
DO  - 10.2991/asei-15.2015.67
ID  - Zheng2015/05
ER  -