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 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 - TY - CONF A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications 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 - CONF A1 - Liehr, Sascha T1 - Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications 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 AN - OPUS4-48774 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schreier, Andy A1 - Liehr, Sascha A1 - Wosniok, Aleksander A1 - Krebber, Katerina T1 - Comparison of solution approaches for distributed humidity sensing in perfuorinated graded-index polymer optical fibers N2 - We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects. T2 - SPIE Optics + Optoelectronics CY - Prague, Czech Republic DA - 01.04.2019 KW - Distributed sensing KW - Humidity KW - Polymer optical fibre PY - 2019 SN - 978-1-5106-2723-9 DO - https://doi.org/10.1117/12.2522307 SN - 0277-786X SN - 1996-756X SP - 1102808-1 EP - 1102808-9 PB - SPIE AN - OPUS4-47844 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, Christos 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 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 - CONF A1 - Liehr, Sascha T1 - Distributed Humidity Sensing in PMMA Polymer Optical Fibers N2 - The distributed measurement of relative humidity is a sought-after capability for a wide range of applications in civil engineering and structural health monitoring. We show that polymethyl methacrylate (PMMA) optical fi-bers can be employed as a sensor medium to conduct distributed humidity measurement by analyzing Rayleigh backscattering traces obtained by OTDR. We make use of the effect that water penetrates the fiber core and directly influences the local fiber attenuation and Rayleigh backscatter coefficient. We conducted distributed backscattering analysis for two different pulse wavelengths: 500 nm and 650 nm. The 650 nm results are susceptible to both, attenuation changes and backscatter changes, whereas backscatter results at 500 nm are not affected by humidity-induced attenuation and only exhibit a change of Rayleigh backscattered power as a function of humidity. The combined measurement and analysis of both parameters at these two wavelengths has the advantage that cross-sensitivities on backscatter change and attenuation, such as strain and tempera-ture changes, could be separated from the humidity response of the fiber. We present laboratory results for a humidity range between 30% and 90% for both pulse wavelengths: including step responses, humidity cycles and hysteresis analysis. In addition to the attenuation and backscatter coefficient dependence, we also analyze optical runtime changes as a function of humidity. POFs have the advantage that they can be directly embed-ded into materials such as concrete or soil to measure water content or localize water ingress. Standard step-index PMMA POFs can be used as a distributed relative humidity sensor up to 200 m distance. T2 - The 28th International Conference on Plastic Optical Fibers CY - Yokohama, Japan DA - 20.11.2019 KW - Distributed humidity sensor KW - PMMA KW - Rayleigh scattering KW - Polymer optical fiber KW - Distributed sensor KW - Optical fiber sensor KW - OTDR PY - 2019 AN - OPUS4-49868 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks N2 - The use of artificial neural networks (ANNs) is demonstrated for efficient real-time data processing in optical fiber sensing applications. Using ANN-based algorithms, two orders of magnitude improved computation time and improved measurement resolution is achieved for distributed strain sensing using the wavelength-scanning coherent optical time domain reflectometry technique. T2 - WORKSHOP - Machine learning in optical analytics CY - Berlin, Germany DA - 13.11.2019 KW - Artificial neural networks KW - Distributed acoustic sensing (DAS) KW - Distributed vibration sensing KW - Signal processing PY - 2019 AN - OPUS4-49869 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -