TY  - JOUR
A1  - Liehr, Sascha
A1  - Jäger, Lena Ann
A1  - Karapanagiotis, Christos
A1  - Munzenberger, Sven
A1  - Kowarik, Stefan
T1  - Real-time dynamic strain sensing in optical fibers using artificial neural networks
T2  - Optics express : the international electronic journal of optics
N2  - We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/49979
SN  - 1094-4087
VL  - 27
IS  - 5
SP  - 7405
EP  - 7425
PB  - Optical Society of America
CY  - Washington
ER  -