Use of fixed wavelength Fibre-Bragg Grating (FBG) filters to capture time domain data from the distorted spectrum of an embedded FBG sensor to estimate strain with an Artificial Neural Network
Use of fixed wavelength Fibre-Bragg Grating (FBG) filters to capture time domain data from the distorted spectrum of an embedded FBG sensor to estimate strain with an Artificial Neural Network
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DOI：
10.1016/j.sna.2012.12.028
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发表时间：
2013-05
影响因子：
4.6
通讯作者：
G. Kahandawa;J. Epaarachchi;Hao Wang;D. Followell;P. Birt
中科院分区：
文献类型：
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作者：
G. Kahandawa;J. Epaarachchi;Hao Wang;D. Followell;P. Birt
众所周知，FBG传感器光谱是失真的，因此难以通过简单地跟踪光谱的峰值点来估计被监测结构中的应变。由于这个问题，从光谱分析仪（OSA）在波长域中获取的传统数据需要进行大量处理，以将其解码为可用的形式，作为人工神经网络（ANN）的输入，人工神经网络是后处理不规则数据集的潜在候选者。本文介绍了一个成功的应用FBG滤波器系统，使用三个固定波长的FBG滤波器捕获实时数据从嵌入式FBG传感器在时域。时域FBG数据进行后处理，使用功率-时间面积积分，以占失真之前，它被输入到人工神经网络。由人工神经网络估计的应变与经验结果相关性很好。
It is well known that an FBG sensor spectrum is distorted, thus making it difficult to estimate strain in monitored structures by simply tracking the peak point of the spectrum. Due to this issue, the traditional data acquired in the wavelength domain from optical spectrum analysers (OSA) needs significant processing to decode it into a useable form as an input to an Artificial Neural Network (ANN), a potential candidate for post-processing irregular data sets. This paper describes a successful application of an FBG filter system using three fixed wavelength FBG filters to capture real-time data from an embedded FBG sensor in the time domain. The time domain FBG data was post-processed using power-time area integration to account for the distortion before it was input into an ANN. The strain estimated by the ANN correlates well with the empirical results.