TY - CONF A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications T2 - Proceedings of International Symposium on Sensor Science N2 - We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable. T2 - 7th International Symposium on Sensor Science CY - Napoli, Italy DA - 09.05.2019 KW - Optical fiber sensor KW - Distributed acoustic sensor (DAS) KW - Optical time domain reflectometry KW - Rayleigh scattering KW - Artificial neural networks KW - Structural health monitoring KW - Seismic measurement PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-487733 DO - https://doi.org/10.3390/proceedings2019015030 SN - 2504-3900 VL - 15 SP - Paper 30, 1 EP - 5 PB - MDPI CY - Basel AN - OPUS4-48773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, C. A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Pithan, L. A1 - Liehr, Sascha A1 - Schreiber, Frank A1 - Kowarik, Stefan T1 - Fast fitting of reflectivity data of growing thin films using neural networks JF - Journal of Applied Crystallography N2 - X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498997 DO - https://doi.org/10.1107/S1600576719013311 SN - 1600-5767 VL - 52 SP - 1342 EP - 1347 PB - Wiley AN - OPUS4-49899 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Jäger, L. A. A1 - Karapanagiotis, Christos A1 - Münzenberger, Sven A1 - Kowarik, Stefan T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks JF - Optics Express 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. KW - Distributed vibration sensing KW - Distributed acoustic sensing KW - Coherent optical time domain reflectometry KW - Optical fiber sensor KW - Artificial neural network KW - Real time measurement KW - Distributed strain sensing PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-474832 UR - https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 DO - https://doi.org/10.1364/OE.27.007405 SN - 1094-4087 VL - 27 IS - 5 SP - 7405 EP - 7425 PB - Optical Society of America AN - OPUS4-47483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -