Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61346
Title: Signal processing using artificial neural network for BOTDA sensor system
Authors: Azad, AK
Wang, L
Guo, N
Tam, HY 
Lu, C 
Issue Date: 2016
Publisher: Optical Society of America
Source: Optics express, 2016, v. 24, no. 6, p. 6769-6782 How to cite?
Journal: Optics express 
Abstract: We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.
URI: http://hdl.handle.net/10397/61346
EISSN: 1094-4087
DOI: 10.1364/OE.24.006769
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