Skip to main content
Article thumbnail
Location of Repository

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

By Gayan C. Kahandawa, Jayantha Ananda Epaarachchi, Hao Wang and Kin Tak Lau

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

Topics: Composite materials, FBG Sensors, Structural health monitoring
Publisher: Scientific.Net
Year: 2013
DOI identifier: 10.4028/www.scientific.net
OAI identifier: oai:vtl.cc.swin.edu.au:swin:49475
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://dx.doi.org/10.4028/www.... (external link)
  • http://hdl.handle.net/1959.3/4... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.