Prediction of obsolete FBG sensor using ANN for efficient and robust operation of SHM systems

Kahandawa, Gayan C. and Epaarachchi, Jayantha A. and Wang, Hao and Lau, K. T. (2013) Prediction of obsolete FBG sensor using ANN for efficient and robust operation of SHM systems. In: 4th Asia-Pacific Workshop on Structural Health Monitoring, 5-7 Dec 2012, Melbourne, Australia.

Abstract

Increased use of FRP composites for critical load bearing components and structures in recent years has raised the alarm for urgent need of a comprehensive health mentoring system to alert users about integrity and the health condition of advanced composite structures. A few decades of research and development work on structural health monitoring systems using Fibre Bragg Grating (FBG) sensors have come to an accelerated phase at the moment to address these demands in advanced composite industries. However, there are many unresolved problems with identification of damage status of composite structures using FBG spectra and many engineering challenges for implementation of such FBG based SHM system in real life situations. This paper details a research work that was conducted to address one of the critical problems of FBG network, the procedures for immediate rehabilitation of FBG sensor networks due to obsolete/broken sensors. In this study an artificial neural network (ANN) was developed and successfully deployed to virtually simulate the broken/obsolete sensors in a FBG sensor network. It has been found that the prediction of ANN network was within 0.1% error levels.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 Trans Tech Publications, Switzerland. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 139.86.2.15-01/05/13,06:14:32).
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 07 Mar 2014 11:22
Last Modified: 07 Feb 2018 02:41
Uncontrolled Keywords: FBG sensors; composite materials; structural health monitoring
Fields of Research : 09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
10 Technology > 1003 Industrial Biotechnology > 100304 Industrial Biotechnology Diagnostics (incl. Biosensors)
03 Chemical Sciences > 0301 Analytical Chemistry > 030107 Sensor Technology (Chemical aspects)
Socio-Economic Objective: B Economic Development > 86 Manufacturing > 8699 Other Manufacturing > 869999 Manufacturing not elsewhere classified
Identification Number or DOI: 10.4028/www.scientific.net/KEM.558.546
URI: http://eprints.usq.edu.au/id/eprint/24934

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