Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network

• Published in: Measurement : journal of the International Measurement Confederation Vol. 46; no. 10; pp. 4045 - 4051
• Main Authors: Kahandawa, Gayan C., Epaarachchi, Jayantha, Wang, Hao, Canning, John, Lau, K.T.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2013
• Subjects: Algorithms; Artificial neural networks; Composite structures; Extraction; FBG sensors; Health monitoring (engineering); Offices; Real time; Sensors; Strain; Structural health monitoring; Composite structures; FBG sensors; Structural health monitoring
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2013.07.029

•Method to interrogate FBG sensors using the fixed wavelength FBG filters.•FBG filter system to capture data from an embedded FBG sensor in the time domain.•FBG data processing using area integration accounting for the distorted spectra.•Estimation of strain using distorted FBG sensor response spectra using ANN.•It was found that the error levels were less than 0.3% in predictions using ANN.
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 algorithm sare 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.