TY - CONF A1 - Karapanagiotis, Christos T1 - Development of artificial neural networks for real time and high resolution strain sensing N2 - We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach. This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest. T2 - Workshop (in collaboration with KIT) CY - Online meeting DA - 27.08.2018 KW - Dynamic strain sensing KW - Machine learning KW - Distributed fiber-optic sensors PY - 2018 AN - OPUS4-52307 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -