Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network
Article
Kahandawa, Gayan C., Epaarachchi, Jayantha, Wang, Hao, Canning, John and Lau, K. T.. 2013. "Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network." Measurement. 46 (10), pp. 4045-4051. https://doi.org/10.1016/j.measurement.2013.07.029
| Article Title | Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network |
|---|---|
| ERA Journal ID | 650 |
| Article Category | Article |
| Authors | Kahandawa, Gayan C. (Author), Epaarachchi, Jayantha (Author), Wang, Hao (Author), Canning, John (Author) and Lau, K. T. (Author) |
| Journal Title | Measurement |
| Journal Citation | 46 (10), pp. 4045-4051 |
| Number of Pages | 7 |
| Year | 2013 |
| Publisher | Elsevier |
| Place of Publication | Amsterdam, Netherlands |
| ISSN | 0263-2241 |
| 1873-412X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.measurement.2013.07.029 |
| Abstract | Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithms are available for using in SHM systems have been developed. 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 predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course. |
| Keywords | composite structures; FBG sensors; structural health monitoring |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| 400999. Electronics, sensors and digital hardware not elsewhere classified | |
| 400101. Aerospace materials | |
| Public Notes | © 2013 Elsevier Ltd. Published version deposited in accordance with the copyright policy of the publisher. |
| Byline Affiliations | Centre of Excellence in Engineered Fibre Composites |
| University of Sydney | |
| Hong Kong Polytechnic University, China | |
| Institution of Origin | University of Southern Queensland |
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