Estimation of strain of distorted FBG sensor spectra using a fixed FBG filter circuit and an artificial neural network

Kahandawa, Gayan C. and Epaarachchi, Jayantha and Lau, K. T. and Canning, John (2013) Estimation of strain of distorted FBG sensor spectra using a fixed FBG filter circuit and an artificial neural network. In: IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2013): Sensing the Future, 2-5 Apr 2013, Melbourne, Australia.

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Abstract

Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in strain predictions.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 IEEE. Published version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - No Department
Date Deposited: 16 Aug 2013 03:16
Last Modified: 29 Mar 2018 00:21
Uncontrolled Keywords: artificial neural network algorithm; controlled environment; fibre Bragg grating sensors; initiation and propagation; structural health monitoring (SHM)
Fields of Research : 09 Engineering > 0912 Materials Engineering > 091202 Composite and Hybrid Materials
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
03 Chemical Sciences > 0301 Analytical Chemistry > 030107 Sensor Technology (Chemical aspects)
Socio-Economic Objective: B Economic Development > 87 Construction > 8703 Construction Materials Performance and Processes > 870399 Construction Materials Performance and Processes not elsewhere classified
Identification Number or DOI: 10.1109/ISSNIP.2013.6529770
URI: http://eprints.usq.edu.au/id/eprint/23922

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