TY - JOUR A1 - Kowarik, Stefan A1 - Hussels, Maria-Teresa A1 - Chruscicki, Sebastian A1 - Münzenberger, Sven A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. T1 - Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis N2 - Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis. KW - Artificial neural networks KW - Distributed fiber optic sensing KW - Distributed acoustic sensing KW - Train tracking PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-502872 DO - https://doi.org/10.3390/s20020450 VL - 20 IS - 2 SP - 450 PB - MDPI CY - Basel, Switzerland AN - OPUS4-50287 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Borchardt, Christopher T1 - Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers N2 - A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification. KW - Neural networks KW - Fiber optic KW - Vibration sensing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-518694 DO - https://doi.org/10.1364/OE.402789 VL - 28 IS - 26 SP - 39325 AN - OPUS4-51869 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Krebber, Katerina T1 - Direct detection based φOTDR using the Kramers-Kronig receiver N2 - A Kramers-Kronig (KK) receiver is applied to a phase-sensitive optical time domain reflectometry based on direct detection. An imbalanced Mach-Zehnder interferometer with a 2× 2 coupler is used in sensing system to encode the phase information into optical intensity. The directly obtained signal is treated as the in-phase component, and the KK receiver provides the quadrature component by Hilbert transform of the obtained signal, so that the optical phase can be retrieved by IQ demodulation. The working principle is well explained, and the obtained phase variance is theoretically analyzed. The experiment demonstrates the functionality of the sensor and validates the theoretical analysis. KW - Kramers-Kronig detector KW - Distributed fiber sensing KW - Acoustic sensing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516803 UR - https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-28-24-37058&id=442839 DO - https://doi.org/10.1364/OE.405723 VL - 28 IS - 24 SP - 37058 EP - 37068 PB - Optical Society of America AN - OPUS4-51680 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Krebber, Katerina T1 - Characterizing detection noise in phase-sensitive optical time domain reflectometry N2 - Phase-sensitive optical time domain reflectometry (φOTDR) is an excellent distributed fiber sensing technique and has been applied in various areas. Its noise is however never been comprehensively studied to the best of our knowledge. The different detection noise sources in such a sensing system are thoroughly investigated. The impacts of thermal noise, shot noise and the beat between signal and the amplified spontaneous emission from a pre-amplifier have been theoretically and experimentally demonstrated. Due to the random nature of the φOTDR signal, the detection noise demonstrates distinct features at different fiber positions in a single measurement. The theoretical analysis and the experimental result explicitly affirm most of the fiber sections, and the difference at some positions may be explained by ambient noise. KW - Noise analysis KW - Acoustic sensing KW - Distributed fiber sensing PY - 2021 DO - https://doi.org/https://doi.org/10.1364/OE.424410 SN - 1094-4087 VL - 29 IS - 12 SP - 18791 EP - 18806 PB - Optical Society of America CY - Washington, DC AN - OPUS4-52757 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Krebber, Katerina T1 - Phase error analysis and unwrapping error suppression in phase-sensitive optical time domain reflectometry N2 - Phase-sensitive optical time domain reflectometry becomes an effective tool to realize distributed sensing, and the optical phase of the received light is usually used to quantify the strain for both dynamic and static measurement. The analysis on the overall phase error has been improved by considering the proportionality of the detection noise to the local optical power. The estimation accuracy is greatly improved by using the proposed theory, the probability density of the estimation accuracy over 99% is > 0.6, ∼39 times larger than the previously reported method. The phase unwrapping may malfunction due to the noisy signal, causing large phase errors. Point break detection algorithms are used to locate the incorrect phase unwrapping points, so the temporal evolution of the phase retrieved at each position can be divided into several sections with different offset. The phase unwrapping error is then suppressed by removing the offset. KW - Phase unwrapping KW - Distributed fiber sensing KW - Acoustic sensing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543919 DO - https://doi.org/10.1364/OE.446517 VL - 30 IS - 5 SP - 6934 EP - 6948 PB - Optica Publishing Group AN - OPUS4-54391 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Shaheen, Sabahat A1 - Hicke, Konstantin T1 - Geometric phase in distributed fiber optic sensing N2 - The geometric phase in the beat signal from coherent interference of two frequency-offset light beams is measured using a novel distributed optical fiber sensing setup. In a fiber optic medium, with changing beam intensities, to the best of our knowledge, ours is the first measurement of the mentioned geometric phase. Experimental results of applying a 100-Hz sinusoidal stimulus to a polarization scrambler and a piezoelectric transducer inline to an optical fiber are presented. The results may enable novel distributed fiber sensing techniques. KW - Distributed fiber optic sensing KW - Geometric phase measurement KW - Distributed vibration sensing KW - C-OTDR PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556053 DO - https://doi.org/10.1364/OL.464259 VL - 47 IS - 15 SP - 3932 EP - 3935 PB - Optica CY - Washington D.C., USA AN - OPUS4-55605 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Shaheen, Sabahat A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Phase-sensitive optical time domain reflectometry based on geometric phase measurement N2 - A phase-sensitive optical time domain reflectometer based on coherent heterodyne detection of geometric phase in the beat signal of light, is reported for the first time to our knowledge. The use of the geometric phase to extract strain makes it immune to polarisation diversity fading. This is because a polarisation mismatch between the interfering beams is not a hindrance to its measurement. The geometric phase is calculated using the amplitude of the beat signal and individual beam intensities without any need for phase unwrapping. It is measured per beat period and can be equated with the traditionally measured dynamic phase with appropriate scaling. The results show that the system based on the geometric phase successfully measures strain, free from polarisation mismatch fading and phase unwrapping errors, providing a completely novel solution to these problems. KW - Fiber optic sensing KW - Distributed acoustic sensing KW - DAS KW - Geometric phase KW - Pancharatnam–Berry phase KW - Berry phase PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570307 DO - https://doi.org/10.1038/s41598-023-29972-4 VL - 13 SP - 1 EP - 11 PB - Nature Portfolio CY - London AN - OPUS4-57030 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Thomas, P. T1 - Phase Error Evaluation via Differentiation and Cross-Multiplication Demodulation in Phase-Sensitive Optical Time-Domain Reflectometry N2 - Phase-sensitive optical time-domain reflectometry (jOTDR) is a technology for distributed vibration sensing, where vibration amplitudes are determined by recovering the phase of the backscattered light. Measurement noise induces phase errors, which degrades sensing performance. The phase errors, using a differentiation and cross-multiplication (DCM) algorithm, are investigated theoretically and experimentally in a jOTDR system based on a phase retrieval configuration consisting of an imbalanced Mach–Zehnder interferometer (IMZI) and a 3 x 3 coupler. Analysis shows that phase error is highly dependent on the AC component of the obtained signal, essentially being inversely proportional to the product of the power of the light backscattered from two locations. An analytical expression was derived to estimate the phase error and was confirmed by experiment. When applied to the same measurement data, the error is found to be slightly smaller than that obtained using in-phase/quadrature (I/Q) demodulation. The error, however, increases for longer measurement times. KW - Distributed fiber sensing KW - Phase-sensitive optical time-domain reflectometry KW - Phase error PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580501 UR - https://www.mdpi.com/2304-6732/10/5/514 DO - https://doi.org/10.3390/photonics10050514 SN - 2304-6732 VL - 10 IS - 5 SP - 1 EP - 14 PB - MDPI CY - Basel AN - OPUS4-58050 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Shaheen, Sabahat A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Blast-Assisted Subsurface Characterisation Using a Novel Distributed Acoustic Sensing Setup Based on Geometric Phases N2 - A novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth’s subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth’s crust are determined using an optimised evolutionary algorithm. KW - Geophysics KW - Geometric Phase KW - Distributed Fiber Optic Sensor KW - Distributed Acoustic Sensing KW - Earthquake Monitoring KW - Seismology PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-591913 DO - https://doi.org/10.3390/s24010030 VL - 24 IS - 1 SP - 1 EP - 12 PB - MDPI AN - OPUS4-59191 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Distributed acoustic sensing to monitor ground motion/movement at multi-frequency bands N2 - A novel distributed acoustic sensing technique is proposed that exploits both phase and amplitude of the Rayleigh backscattered light to quantify the environmental variation. The system employs a wavelength-scanning laser and an imbalanced Mach-Zehnder interferometer to acquire the reflection spectra and the phase of the detected light, respectively. Fading-free and low-frequency measurements are realized via the crosscorrelation of the reflection spectra. The discrete crosscorrelation is used to circumvent the nonlinear frequency sweeping of the laser. Based on the phase of the backscattered light, it is possible to quantify fast environmental variations. The whole system requires no hardware modification of the existing system and its functionality is experimentally validated. The proposed system has the potential to monitor ground motion/movement at very low frequency band like subsidence around mining areas and at high frequency band like earthquakes and vibrations induced by avalanches. KW - Distributed acoustic sensing KW - DAS KW - Distributed fiber optic sensing KW - Ground motion detection KW - Subsidence monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596129 DO - https://doi.org/10.1109/JLT.2024.3358495 VL - 42 IS - 18 SP - 1 EP - 8 PB - Optical Society und IEEE Photonics Society AN - OPUS4-59612 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shaheen, Sabahat T1 - Photonic crystal fibers for microwave signal processing N2 - We present a novel design of an optical True Time Delay Line based on a 19-core Photonic Crystal Fiber that operates in a broad radiofrequency signal processing range from 1 to 67 GHz on a 10-km link, thus enabling simultaneous signal distribution and processing. T2 - 2021 IEEE Photonics Conference (IPC) CY - Vancouver, BC, Canada DA - 18.10.2021 KW - Delay lines KW - Photonic crytal fibers KW - Optical fibers PY - 2021 SN - 978-1-6654-1601-6 DO - https://doi.org/10.1109/IPC48725.2021.9592934 SP - 1 EP - 3 PB - IEEE AN - OPUS4-56582 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zabihi, Mohammadmasoud A1 - Krebber, Katerina T1 - Approach for Faster Signal Processing in Փ-OTDR Systems Based on Directional Measurement of Fiber Slices Interaction N2 - In this study, a signal processing approach for heterodyne Փ-OTDR and C-OTDR systems that can obtain external perturbation and its frequency content in a faster way is proposed. We can detect vibrations with the same or better SNR using this processing approach, only with a single step and fast calculation. Fig. 12 shows the comparison of the normalized measurement time for the gamma matrix method and the conventional method throughout the three experiments. The speed of processing using a gamma matrix is ~35%–50% faster compared to a conventional method in high frequency test (PZT), low frequency test (walking) and street monitoring test. The processing speed in low frequency test is normally a bit lower than the similar high frequency one, since we must use higher number of time frames. Also, in street test we have higher speed because we can select wider gauges. KW - Փ-OTDR PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560492 DO - https://doi.org/10.1364/OPTCON.456421 VL - 1 IS - 8 SP - 1837 EP - 1847 PB - Optica Publishing Group CY - Washington, DC AN - OPUS4-56049 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zabihi, Mohammadmasoud A1 - Krebber, Katerina T1 - Laser source frequency drift compensation in Φ-OTDR systems using multiple probe frequencies N2 - In this study, an approach for mitigation of LSFD in Φ-OTDR systems was proposed. By using one probe pulse as a reference in a system with two or more probe frequencies, we can remove unwanted low frequency noise that is originally caused by the laser source. Although LSFD is problematic issue, it is not the only source of low frequency noise. Change in temperature, humidity, physical surroundings, environment, etc., can result in such noises. The proposed method; however, is expected to highly suppresses all of these effects, regardless of their source, either in the time or frequency domain. For comparing reference and probe signals, there are sophisticated methods, rather than normal differentiation, available for use. Some of these methods have a close relationship with CPD methods and can further enhance the results. These methods can be further discussed in an independent research or future work. The proposed method is very helpful for increasing accuracy in passive and active seismic monitoring, reservoir monitoring, underwater monitoring, etc. It also helps using relatively simpler laser sources and make the whole system price-efficient, as well as processing data without lengthy compensation algorithms. KW - Frequency Drift KW - Փ-OTDR KW - LSFD PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560517 DO - https://doi.org/10.1364/oe.460302 SN - 1094-4087 VL - 30 IS - 11 SP - 19990 EP - 19998 PB - Optica CY - Washington, DC AN - OPUS4-56051 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Krebber, Katerina T1 - Suppression of Rayleigh fading induced errors in φOTDR by different pulse widths for improving the reliability of civil infrastructure monitoring N2 - Rayleigh fading is a widely observed phenomenon in the many fields, such as wireless communication and optical imaging. It is also the main factor limiting the performance of the phase-sensitive optical time domain reflectometry (φOTDR). The low SNR at the fading points results in a large measurement error, severely affecting the reliability of civil infrastructure monitoring. The proposed method involves changing the pulse width during measurements to suppress the impact of fading. Experimental result shows that the number of fading points is greatly reduced by ∼ 96 % and the measurement error is reduced by more than 5 times. Unlike existing methods, this approach requires no hardware modifications, making it applicable to almost all current phase-based φOTDR systems. The versatility and effectiveness of this method make it an excellent candidate for infrastructure monitoring and related fields. KW - Distributed fiber sensing KW - Optical fiber sensing KW - Phase-sensitive optical time domain reflectometry KW - Infrastructure monitoring KW - Rayleigh fading PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-614208 DO - https://doi.org/10.1016/j.optlastec.2024.111927 SN - 1879-2545 VL - 181 SP - 1 EP - 9 PB - Elsevier Ltd. AN - OPUS4-61420 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Distributed fiber optic sensing for monitoring of underground facilities N2 - Distributed fiber optic sensing (DFOS) technique is a promising and robust non-destructive testing tool that can seamlessly acquire environmental conditions over large scales. Therefore, it has found extensive applications in structural health monitoring. Its appeal for monitoring underground facilities lies in the intrinsic properties of the optical fiber, such as immunity to magnetic interference, small size, chemical inertia, etc. This paper provides a concise overview of DFOS applications in underground facility monitoring. Following a brief introduction to the workingbnprinciple of the DFOS technique, various examples are provided to demonstrate how distributed fiber sensors contribute to monitoring underground facilities. The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement detection. Furthermore, the paper identifies several directions for enhancing the DFOS system. T2 - SPIE Photonics Europe CY - Strasbourg, France DA - 07.04.2024 KW - Structure health monitoring KW - Geothermal borehole KW - Distributed fiber optic sensing KW - Ground movement KW - Leakage detection PY - 2024 DO - https://doi.org/10.1117/12.3023571 SN - 0277-786X VL - 13001 SP - 1 EP - 10 PB - SPIE AN - OPUS4-60384 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Christensen, J. B. A1 - Jørgensen, A. A. A1 - Vandborg, M. H. A1 - Thomas, P. J. A1 - Lu, Xin A1 - Failleau, G. A1 - Eisermann, R. A1 - Grüner-Nielsen, L. A1 - Balslev-Harder, D. A1 - Lassen, M. A1 - Krenek, S. T1 - Fiber-artefact methodology and calibration framework for Brillouin-based fiber sensing N2 - We propose, and demonstrate, the use of a fiber-optical measurement artefact as a metrological tool for traceable distance calibration of distributed optical fiber sensors. The constructed fiber artefact consists of a lead-in fiber coupled to a fiber loop using a 3-dB coupler and is used to calibrate both a home-build Brillouin-OTDR setup and a custom version of a commercial Brillouin-OTDR interrogator build for distributed temperature sensing. For both interrogators, we demonstrate distance calibrations with 1-meter uncertainty (k=1) in the offset length and 0.1 % (1 m/km) uncertainty (k = 1) in the distance scale factor. In addition, it is shown that the fiber artefact can be used to assess undesired distance-dependent measurement biases. KW - Distributed temperature sensing KW - Metrology KW - Distributed optical fiber sensing KW - Brillouin optical time-domain reflectrometry PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-618853 DO - https://doi.org/10.1364/OE.544659 SN - 1094-4087 VL - 32 IS - 26 SP - 45483 EP - 45493 PB - Optica Publishing Group AN - OPUS4-61885 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hüsken, Götz A1 - Pirskawetz, Stephan A1 - Hofmann, Detlef A1 - Basedau, Frank A1 - Gründer, Klaus-Peter A1 - Kadoke, Daniel T1 - The load-bearing behaviour of a reinforced concrete beam investigated by optical measuring techniques N2 - Bending beams and slabs are typical examples for structural elements used for reinforced concrete structures such as bridge girders, T-beams and bridge decks. Their strength related failure modes at maximum loading can be divided into bending and shear failure. The failure of beams loaded in bending can occur with or without indication. Therefore, conventional design concepts aim on failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexural compression failure. These indicating factors can also be used for Structural Health Monitoring (SHM) of civil infrastructure systems (e.g. bridges) to identify structural changes. In this context, non-destructive testing (NDT) methods offer different techniques for measuring deflections or crack formation and opening. However, profound knowledge on the determining failure modes of bending beams and their detection by NDT methods is required for the reliable application of SHM. Different NDT methods have been used in this study for analysing the load-bearing behaviour of a reinforced concrete beam in bending. The different measuring techniques are briefly described and their applicability is discussed by means of experimental results. For this purpose, the load-bearing behaviour of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by optical NDT and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending beam was equipped with two single-mode (SM) sensor fibres. One fibre served as Distributed Optical Fibre Sensor (DOFS), whereas the other fibre contained Fibre Bragg Grating (FBG) sensors. In addition, optical deformation measurements using Digital Image Correlation (DIC) and Stereophotogrammetry (SP) were conducted. KW - Concrete beam KW - Bending KW - Digital image correlation KW - Stereophotogrammetry KW - Distributed fibre optic sensor KW - Fibre bragg grating PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524762 DO - https://doi.org/10.1617/s11527-021-01699-6 VL - 54 IS - 3 SP - Article 102 PB - Springer AN - OPUS4-52476 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Breithaupt, Mathias A1 - Strangfeld, Christoph T1 - Distributed Humidity Sensing in Concrete Based on Polymer Optical Fiber N2 - We present a preliminary investigation on distributed humidity monitoring during the drying process of concrete based on an embedded polymer optical fiber (POF). The water dissipated into the POF changes several properties of the fiber such as refractive index, scattering coefficient and attenuation factor, which eventually alters the Rayleigh backscattered light. The optical time Domain reflectometer (OTDR) technique is performed to acquire the backscattered signal at the wavelengths 650 nm and 500 nm, respectively. Experimental results show that the received signal increases at 650 nm while the fiber attenuation factor clearly increases at 500 nm, as the concrete dries out. In the hygroscopic range, the information retrieved from the signal change at 650 nm agrees well with the measurement result of the electrical humidity sensors also embedded in the concrete sample. KW - Distributed fiber optic sensing KW - Distributed humidity sensing KW - Polymer optical fibers KW - Concrete drying KW - Material moisture KW - Embedded humidity sensors PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-537644 DO - https://doi.org/10.3390/polym13213755 SN - 2073-4360 VL - 13 IS - 21 SP - 3755 PB - MDPI CY - Basel, Switzerland AN - OPUS4-53764 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Martynec, T. A1 - Karapanagiotis, Christos A1 - Klapp, S. H. L. A1 - Kowarik, S. T1 - Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth N2 - Machine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters. KW - Machine learning KW - Surface migration barriers KW - Material engineering PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-532523 UR - https://www.nature.com/articles/s43246-021-00188-1 DO - https://doi.org/10.1038/s43246-021-00188-1 SN - 2662-4443 VL - 2 SP - 1 EP - 9 PB - Springer Nature CY - London AN - OPUS4-53252 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Eine Sammlung durch Maschinelles Lernen unterstützter Verteilter Faseroptischer Sensoren für Infrastrukturmonitoring T1 - A collection of machine learning assisted distributed fiber optic sensors for infrastructure monitoring N2 - In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures. N2 - In diesem Beitrag stellen wir eine Sammlung von verteilten faseroptischen Sensoren (DFOS) vor, die mit Hilfe von Maschinellem Lernen arbeiten und für Anwendungen im Bereich der Infrastrukturüberwachung geeignet sind. Wir setzen hierbei fortschrittliche Signalverarbeitung auf der Grundlage Künstlicher Neuronaler Netze ein, um die Leistungsfähigkeit dynamischer DFOS für die Messung von Dehnungen und Vibrationen zu verbessern. Insbesondere Künstliche Neuronale Netze (ANNs) liefern im Vergleich zu konventionellen und rechenintensiven Korrelations- und Linearisierungsalgorithmen geringere Dehnungsfehler und beschleunigen die Signalverarbeitung, so dass eine Dehnungsüberwachung in Echtzeit möglich ist. Darüber hinaus wenden wir Convolutional Neural Networks (CNNs) an, um dynamische DFOS-Signale zu entrauschen und damit nutzbare Messlängen von bis zu 100 km zu ermöglichen. Es werden Anwendungsbeispiele dieser durch Maschinelles Lernen unterstützten dynamischen DFOS in den Bereichen des Straßenverkehrsmonitorings und der Zug- und Gleisüberwachung aufgezeigt. Im Bereich der statischen DFOS wird Maschinelles Lernen auf das Verfahren der Optischen Brillouin-Frequenzbereichsanalyse (BOFDA) angewendet. Insbesondere CNN erweisen sich hier als sehr robust gegenüber verrauschten Spektren und tragen zu deutlich kürzeren Messzeiten bei. Darüber hinaus werden verschiedene Algorithmen des maschinellen Lernens (lineare und polynome Regression, Entscheidungsbäume, ANNs) angewandt, um das bekannte Problem der Querempfindlichkeit bei DFOS in den Fällen zu lösen, in denen Temperatur und Feuchtigkeit gleichzeitig gemessen werden sollen. Die hier vorgestellten, durch Maschinelles Lernen unterstützten, DFOS können zu einer verbesserten, kostengünstigen und zuverlässigen Überwachung von Infrastrukturen beitragen. KW - Artificial neural networks KW - Brillouin sensing KW - Distributed acoustic sensing KW - Fibre optic sensors KW - Infrastructure monitoring PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-569044 DO - https://doi.org/10.1515/teme-2022-0098 SN - 0171-8096 SP - 1 EP - 19 PB - De Gruyter AN - OPUS4-56904 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Krebber, Katerina T1 - Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors N2 - This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area. KW - Distributed fiber optic sensors KW - BOTDA KW - BOFDA KW - Machine learning KW - Strain and temperature measurements KW - Artificial neural networks PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580145 DO - https://doi.org/10.3390/s23136187 VL - 23 IS - 13 SP - 1 EP - 26 PB - MDPI AN - OPUS4-58014 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Machine learning assisted BOFDA for simultaneous temperature and strain sensing in a standard optical fiber N2 - We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µɛ to 1380 µɛ, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µɛ, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor’s performance is provided. KW - Machine learning KW - Brillouin distributed fiber optic sensors KW - Temperature and strain sensing KW - BOFDA PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-569155 DO - https://doi.org/10.1364/OE.480224 SN - 1094-4087 VL - 31 IS - 3 SP - 5027 EP - 5041 PB - OPTICA AN - OPUS4-56915 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Wosniok, Aleksander A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis N2 - To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential. KW - Fiber-optic sensors KW - Machine learning KW - Temperature and strain monitoring KW - Brillouin distributed sensing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524631 DO - https://doi.org/10.3390/s21082724 VL - 21 IS - 8 SP - 2724 PB - MDPI AN - OPUS4-52463 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Wosniok, Aleksander A1 - Krebber, Katerina T1 - Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach N2 - We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects. KW - Fiber optics sensors KW - Machine learning KW - Brillouin distributed sensing KW - Humidity sensing KW - Simultaneous temperature and humidity sensing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546294 DO - https://doi.org/10.1364/OE.453906 VL - 30 IS - 8 SP - 12484 EP - 12494 PB - OPTICA AN - OPUS4-54629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Temperature and humidity discrimination in Brillouin distributed fiber optic sensing using machine learning algorithms N2 - In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection. T2 - SPIE Photonics Europe 2022 CY - Strasbourg, France DA - 03.04.2022 KW - Distributed Brillouin sensing KW - Machine learning KW - Artificial neural networks KW - Simultaneous temperature and humidity sensing KW - BOFDA PY - 2022 DO - https://doi.org/10.1117/12.2620985 VL - 12139 SP - 1 EP - 7 PB - SPIE AN - OPUS4-54861 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hüsken, Götz A1 - Shamsuddoha, Md A1 - Pirskawetz, Stephan A1 - Hofmann, Detlef A1 - Baeßler, Matthias A1 - Kühne, Hans-Carsten T1 - Potential of a Repair System for Grouted Connections in Offshore Structures: Development and Experimental Verification N2 - Grouted connections are intensively used in offshore rigs, platforms as well as jacket and monopile offshore wind turbine structures. Being located in remote offshore conditions, these connections can experience considerable adverse loading during their lifetimes. Degradation was reported inside similar connections, which were installed in the last three decades. Grouting in the offshore sites may often be proven difficult, which eventually leads to reduced load-bearing capacity of connections in the long run. Thus, repair and rehabilitation of such connections should be planned ahead to minimize operational delays and costs. In this study, scaled grouted connections were manufactured using a novel mould, whose integrity were monitored using digital image correlation (DIC). The connections were loaded under static load to visualize the main failure pattern using distributed fibre optic sensors and acoustic emission (AE) analysis. Grouted connections were then repaired using a cementitious injectable grout. The effectiveness of the grout injection was monitored using dye penetration technique. Finally, specimens are reloaded to identify the potential of such repair for grouted connections. KW - Offshore KW - Grouted connection KW - Fibre optic sensors KW - Acoustic emission analysis KW - Cracks KW - Repair KW - Rehabilitation KW - Static loading PY - 2021 DO - https://doi.org/10.1016/j.marstruc.2021.102934 VL - 77 SP - 102934 PB - Elsevier Ltd. AN - OPUS4-52059 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Schukar, Marcus A1 - Krebber, Katerina T1 - Verteilte faseroptische Sensoren zur Zustandsüberwachung von Verbundwerkstoff-Druckbehältern T1 - Distributed fiber optic sensors for structural health monitoring of composite pressure vessels N2 - In diesem Beitrag geben wir einen umfassenden Überblick über unsere Forschung auf dem Gebiet der verteilten faseroptischen Sensorik für die strukturelle Zustandsüberwachung von Wasserstoffdruckbehältern aus Verbundwerkstoffen. Insbesondere zeigen wir, wie die Integration von faseroptischen Sensoren in Druckbehälter aus Verbundwerkstoffen die Sicherheit erhöht und gleichzeitig die Wartungskosten senkt. Die geringe Größe von Lichtwellenleitern ermöglicht ihre Integration in Verbundwerkstoffstrukturen während des Herstellungsprozesses, wodurch eine kontinuierliche Überwachung sowie eine präzise Erkennung und Lokalisierung von Strukturschäden während des Betriebs der Druckbehälter ermöglicht wird. Wir erörtern auch das Potenzial modernster Signalverarbeitungsmethoden und des maschinellen Lernens für die Weiterentwicklung der vorausschauenden Instandhaltung. Die von uns vorgestellten Anwendungen von faseroptischen Sensoren zeigen, dass sie einen wichtigen Beitrag zur Energiewende hin zu erneuerbaren Energien leisten können. KW - Verteilte faseroptische Sensoren KW - Verbundwerkstoffe KW - Wasserstoff-Druckbehälter KW - Strukturüberwachung KW - Maschinelles Lernen PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596290 DO - https://doi.org/10.1515/teme-2023-0170 SN - 0171-8096 SP - 1 EP - 12 AN - OPUS4-59629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Hierzegger, R. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy KW - Modular Production PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 DO - https://doi.org/10.1109/TETC.2021.3131371 SN - 2168-6750 VL - 10 IS - 1 SP - 87 EP - 98 PB - IEEE AN - OPUS4-53941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Popiela, Bartosz A1 - Günzel, Stephan A1 - Schukar, Marcus A1 - Mair, Georg W. A1 - Krebber, Katerina A1 - Seidlitz, Holger T1 - Impact of internal pressure control during manufacturing on residual stresses and safety performance of type 4 pressure vessels N2 - Composite pressure vessels are commonly manufactured using the wet filament winding process, where various process parameters can influence the performance of the finished component. In this study two designs of wet filament wound 6.8-liter type 4 composite pressure vessels were manufactured. Both differ only by the internal pressure used during the filament winding, which primarily influences the residual stress state in the composite structure. An extensive experimental study was carried out, including 10 slow burst tests and strain measurements with fiber optic sensors. Significant differences can be observed in the performance of the two designs even though the used stacking sequence, materials and other manufacturing parameters are the same for both designs. A discussion of the differences in the behavior of both cylinder types is provided, including the strain distribution in slow burst tests and failure mechanism. KW - Residual stresses KW - Slow burst tests KW - Filament winding KW - Type 4 composite pressure vessels KW - Hydrogen PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626663 DO - https://doi.org/10.1016/j.jcomc.2025.100581 SN - 2666-6820 VL - 17 SP - 1 EP - 9 PB - Elsevier B.V. CY - Amsterdam, Netherlands AN - OPUS4-62666 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wang, Kun A1 - Lu, Xin A1 - Schukar, Marcus A1 - Hicke, Konstantin T1 - Water absorption effects on distributed temperature sensing using polyimide-coated optical fiber N2 - Polyimide-coated fibers are becoming more popular for distributed temperature sensing (DTS) because this coating can withstand much higher temperatures than the standard acrylate coating. As a hygroscopic material, polyimide can absorb water from the air, changing its properties, which may result in a modified temperature response of the sensing fiber. This study investigates the effect of water absorption on the performance of polyimide-coated optical fibers with different sizes and properties. The thermal response of these fibers was determined experimentally from 20°C to 90°C at a broad relative humidity level ranging from 10% to 90%. The results show that all the fibers experienced a decrease in temperature sensitivity as humidity increased, with the most noticeable non-linear spectral shift observed at higher humidity levels. These findings highlight the importance of optimizing fiber design and coating properties to balance stability and sensitivity, ensuring the reliable performance of DTS systems under extreme environmental conditions. T2 - 29th International Conference on Optical Fiber Sensors CY - Porto, Portugal DA - 26.05.2025 KW - Humidity sensing KW - Distributed temperature sensing KW - Optical fiber sensors KW - Temperature sensing KW - Water absorption KW - Polyimide-coated fiber PY - 2025 SN - 978-1-5106-9187-2 DO - https://doi.org/10.1117/12.3062748 VL - 13639 SP - 136393W-1 EP - 136393W-4 PB - SPIE AN - OPUS4-63642 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Schukar, Marcus T1 - Humidity response analysis of optical fibers with hygroscopic coatings based on Lamé’s equations N2 - Optical fibers with hygroscopic coatings are widely used for humidity sensing, where the coating expands upon absorbing water, inducing strain in the fiber. This strain is then used to determine humidity. However, previous studies have oversimplified the strain generation process. A comprehensive three-dimensional model of the mechanical interaction between the coating and the fiber is built based on Lamé’s equations. An analytical expression for the induced strain is derived. The proposed model predicts larger humidity-induced strain compared to the reported ones, given the same Young’s modulus or coefficient of humidity expansion for the coating. Interestingly, the effect of coating thickness on strain response are quite similar for both methods. Experimental validation using fibers with a polyimide coating shows strong agreement with the theoretical predictions. T2 - 29th International Conference on Optical Fiber Sensors CY - Porto, Portugal DA - 26.05.2025 KW - Reflectometry KW - Distributed fiber sensing KW - Humidity sensors KW - Lamé’s equations KW - Optical fiber sensors KW - Structural health monitoring KW - Rayleigh scattering PY - 2025 SN - 978-1-5106-9188-9 DO - https://doi.org/10.1117/12.3060891 VL - 13639 SP - 1 EP - 4 PB - SPIE AN - OPUS4-63257 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Hicke, Konstantin T1 - Polarization-diversity-detection-based wavelength-scanning coherent-optical-time-domain reflectometer and its application for bus monitoring N2 - Although wavelength scanning coherent optical time-domain reflectometry (WS-COTDR) system is immune to fading effect, it suffers from occasional and localized large errors caused by the false peak in the correlation spectrum due to the large signal generated in the random interference of the backscattered light. Polarization diversity detection is applied to a standard WS-COTDR system to suppress large measurement errors. In this way, the orthogonally polarized components of backscattered light can be obtained and processed separately. Due to the birefringence of the sensing fiber, the components experience different interference processes, so the average of their correlation spectrum can suppress the false peaks and finally reduce the occurrence of larger errors. The effectiveness of the proposed method is validated in laboratory for monitoring of sinusoidal vibrations and in field for tracking the bus movement via a dark fiber. T2 - 29th International Conference on Optical Fiber Sensors CY - Porto, Portugal DA - 26.05.2025 KW - Traffic monitoring KW - Coherent optical time domain reflectometry KW - Distributed fiber sensing KW - Polarization diversity detection PY - 2025 SN - 978-1-5106-9187-2 DO - https://doi.org/10.1117/12.3060889 VL - 13639 SP - 1 EP - 4 PB - SPIE AN - OPUS4-63258 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Munzke, Dorit A1 - Duffner, Eric A1 - Eisermann, René A1 - Schukar, Marcus A1 - Schoppa, André A1 - Szczepaniak, Mariusz A1 - Strohhäcker, J. A1 - Mair, Georg W. T1 - Monitoring of type IV composite pressure vessels with multilayer fully integrated optical fiber based distributed strain sensing N2 - We present the results of distributed fiber optic strain sensing for condition monitoring of a hybrid type IV composite fully wrapped pressure vessel using multilayer integrated optical fibers. Distributed strain sensing was performed for a total number of 252,000 load cycles until burst of the vessel. During this ageing test material fatigue could be monitored and spatially localized. Critical material changes were detected 17,000 cycles before material failure. Results have been validated by acoustic emission analysis. T2 - 12th International Conference on Composite Science and Technology (ICCST12) CY - Sorrento, Italy DA - 08.05.2019 KW - Hybrid composite pressure vessel KW - Distributed fiber optic sensing KW - Acoustic emission analysis KW - Structural health monitoring PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516772 DO - https://doi.org/10.1016/j.matpr.2020.02.872 SN - 2214-7853 VL - 34 SP - 217 EP - 223 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-51677 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-507508 DO - https://doi.org/10.1007/s00216-020-02687-5 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Schukar, Marcus T1 - Humidity response of optical fibres with hygroscopic coatings and its temperature dependence N2 - Optical fibres with hygroscopic coatings are widely used in structural health monitoring to acquire the humidity information. Water absorption-induced coating expansion leads to a strain change in the silica fibre, which can be measured by point and distributed strain fibre sensors and used to quantify the environmental humidity. In this paper, the strain transfer from the coating to the fibre is described by both 1D and 3D models. The impact of the coating property on the humidity induced strain is theoretically analysed. Since the properties of the sensing fibre are temperature-dependent, the humidity response obtained by the strain-based approach may change under different thermal conditions. The humidity response of fibres with different polyimide coatings is characterized by a commercial optical frequency domain reflectometer under different temperature conditions. The experimental results confirm the temperature dependence of the humidity response as the sensitivity of all the tested fibres decreases as the temperature increases. KW - Rayleigh scattering KW - Humidity sensor KW - Optical fibre sensors KW - Optical frequency domain reflectometer PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629735 DO - https://doi.org/10.1088/2515-7647/adc85f VL - 7 IS - 2 SP - 1 EP - 11 PB - IOP Publishing Ltd CY - Bristol, UK AN - OPUS4-62973 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Chruscicki, Sebastian A1 - Schukar, Marcus A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring N2 - Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks). KW - Destructive testing KW - Structural health monitoring KW - Distributed fiber sensing KW - Distributed acoustic sensing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547451 DO - https://doi.org/10.3390/s22093434 VL - 22 IS - 9 SP - 3434 PB - MDPI AN - OPUS4-54745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Breithaupt, Mathias A1 - Duffner, Eric A1 - Schukar, Marcus T1 - Real-time monitoring of hydrogen composite pressure vessels using surface-applied distributed fiber optic sensors N2 - In this paper, we report to the best of our knowledge for the first time on continuous real-time monitoring of composite overwrapped pressure vessels (COPVs) designed for hydrogen storage using surface-applied distributed fiber optic sensors (DFOS). We conducted continuous and real-time DFOS measurements during pressure cycling tests consisting of periodic pressure fluctuations between 20 bar and 875 bar, with a rate of 5 cycles min−1. During pressure cycling, the DFOS system measured strain changes, that under normal operating conditions were linearly correlated to changes in pressure. To detect and quantify damage-related anomalies, we trained a simple regression model to predict strain from pressure data and used the difference between predicted and measured values as a damage indicator. With our approach, the DFOS system not only detected and localized the damage but also continuously tracked its evolution in real time under dynamic pressure conditions. Furthermore, unlike previous studies where optical fibers were embedded within the composite structure, we applied them on the COPV surface, reducing both implementation cost and time while eliminating the need to modify the COPV manufacturing process. Based on our results, we are confident that DFOS can enhance safety and facilitate the transition from time-consuming periodic inspections to more efficient, machine learning-based predictive maintenance. KW - Fiber optic sensors KW - Hydrogen KW - Structural health monitoring KW - Pressure vessels KW - Predictive maintenance PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627644 DO - https://doi.org/10.1088/2515-7647/adb9ac SN - 2515-7647 VL - 7 IS - 2 SP - 2 EP - 10 PB - IOP Publishing Ltd CY - Bristol, UK AN - OPUS4-62764 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Krenek, S. A1 - Eisermann, R. A1 - Failleau, G. A1 - Lu, Xin A1 - Thomas, P. A1 - Kjeldsen, H. A1 - Anhalt, K. T1 - Fibre-optic thermometry to support the clean energy transition N2 - The measurement and control of temperature plays a key role in achieving the European Green Deal targets for a low carbon energy system. Fibre-optic thermometry is an emerging technology that can improve temperature measurement in extreme environments for energy providers and industry due to its distributed sensing and immunity to electromagnetic fields. Various applications for optimisation and monitoring in the energy sector are described, covering the whole range from energy generation to transmission and consumption. However, fibre-optic thermometers have cross sensitivities to other quantities (e.g., strain and humidity) and ageing effects that need to be investigated, quantified and minimised to obtain traceable and reliable measurements. This is particularly important so that applications in critical infrastructure can benefit from future measurements that are not possible with conventional sensors. The European INFOTherm project aims to overcome the limitations that currently prevent the widespread use of fibre-optic thermometry by creating a dedicated European metrology infrastructure for research, development and calibration. First results on measurement uncertainty, improvement of measurement techniques and practical field tests are presented. KW - Industrial processes optimisation KW - Fibre-optic thermometry KW - Distributed temperature sensing KW - Traceability KW - Thermal energy storage KW - Electrical grid resilience PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640075 DO - https://doi.org/10.1515/teme-2025-0044 SN - 2196-7113 VL - 92 IS - 9-10 SP - 392 EP - 405 PB - De Gruyter Brill AN - OPUS4-64007 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Christensen, J. A1 - Thomas, P. A1 - Failleau, G. A1 - Eisermann, R. A1 - Hicke, Konstantin A1 - Krenek, S. T1 - Fiber artefact for performance evaluation of time domain distributed fiber sensor interrogators N2 - Distributed fiber sensing (DFS) is a powerful tool for structural health monitoring (SHM), allowing continuous and seamless measurements of temperature and strain along the fiber. The spatial accuracy of a DFS interrogator, as a key parameter of the system, is vital for precisely locating structural perturbations or defects. Its evaluation and calibration methods however attract little attention. A fiber optic artefact based on a fiber loop is developed to evaluate distance accuracy and signal quality for both self-developed and commercial sensing systems based on Rayleigh, Raman, and Brillouin scattering effects, respectively. The measured distance is corrected to remove the influence of the pulse width. Additionally, the obtained SNRs are compared for different loop trips and pulse widths, assisting to assess signal quality for SHM applications. KW - Structural health monitoring KW - Distributed fiber sensing KW - Distributed temperature sensing KW - Fiber artefact KW - Spatial correction PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-648966 DO - https://doi.org/10.1088/1361-6501/ae214e VL - 36 IS - 11 SP - 1 EP - 10 PB - IOP Publishing Ltd CY - Bristol, UK AN - OPUS4-64896 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wang, Kun A1 - Schukar, Marcus A1 - Hicke, Konstantin A1 - Lu, Xin T1 - Effect of humidity on fiber-optic temperature sensing N2 - This study investigates the influence of ambient humidity on the temperature sensitivity, measurement accuracy, and uncertainty of optical fibers with different coatings, using a Rayleigh-based distributed sensing technique. Polymer-coated fibers (acrylate and polyimide) and metal-coated fibers (copper and gold) were tested under controlled humidity (30%–90% RH) and temperature (20–60 °C) conditions. Rayleigh-based measurements revealed a slight but consistent decrease in temperature sensitivity with increasing humidity for all polymer-coated fibers, attributed to humidity-induced changes in coating stiffness and strain transfer. In contrast, metal-coated fibers exhibited humidity-independent behavior and superior stability. These findings highlight the non-negligible role of ambient humidity in fiber-optic temperature sensing, particularly in Rayleigh-based systems using hygroscopic coatings. The results provide practical guidance for fiber and coating selection in humid environments and offer broader insight into humidity–strain–temperature coupling mechanisms relevant to other fiber-optic sensing mechanisms. KW - Distributed sensing KW - Humidity effect KW - Temperature sensing KW - Optical fiber coatings KW - Temperature uncertainty PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-650040 DO - https://doi.org/10.1016/j.measurement.2025.119905 SN - 0263-2241 VL - 261 SP - 1 EP - 9 PB - Elsevier Ltd. AN - OPUS4-65004 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Hicke, Konstantin T1 - Error suppression in wavelength scanning coherent optical time domain reflectometry by polarization diversity detection N2 - Wavelength scanning coherent optical time domain reflectometry (WS-COTDR) is a popular distributed fiber sensing technique that quantifies environmental changes based on the frequency shift of Rayleigh reflection spectra which are usually obtained by cross correlation. This method can cause large errors due to the spikes in the spectrum caused by the random interference of the Rayleigh backscattered light and the uncorrelated section in the reflection spectrum. Polarization diversity detection is applied to suppress this type of error. Two orthogonally polarized components of the Rayleigh backscattered light are detected in a single sensing system and processed separately by cross correlation. The correlation spectra of the two polarizations are averaged to determine the frequency shift, effectively mitigating the large error. Meanwhile, the polarization dependence helps avoid the large errors caused by Fresnel reflection and restrain null sensitivity positions, where the frequency shift is difficult to be determined due to the similarity of the spectra. The sensing performance improvement is experimentally validated, and the proposed system is tested in the field to monitor traffic along a busy street in Berlin, demonstrating its functionality and feasibility in practice. KW - Traffic monitoring KW - Coherent optical time domain reflectometry KW - Distributed fiber sensing KW - Polarization diversity detection KW - Optical fiber sensing PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630320 DO - https://doi.org/10.1016/j.measurement.2025.117733 SN - 1873-412X VL - 253 SP - 1 EP - 11 PB - Elsevier Ltd. AN - OPUS4-63032 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Königsbauer, Korbinian A1 - Hicke, Konstantin T1 - Improved phase retrieval method for system simplification or fading suppression in interferometer-based φOTDR systems N2 - Phase sensitive optical time domain reflectometry (φOTDR) systems based on different types of interferometers for phase retrieval typically require two or three photodetectors to record the outputs from the interferometer. A novel signal processing principle is proposed for phase retrieval by taking the difference between two outputs as the quadrature component and reconstructing the in-phase component via Hilbert transformation of the Q component for IQ demodulation. Thus, only one balanced photodetector or two standard photodetectors are need, reducing system complexity and data volume. This principle can also be used to suppress fading effect for the traditional three-detector φOTDR systems by selecting optimal phases across detector pairs. Experiments with a φOTDR systems based on an imbalanced Mach-Zehnder interferometer validate the feasibility of this method and demonstrate a high fading suppression of about 90%. KW - Phase retrieval KW - Distributed fiber sensing KW - Structural health monitoring PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652630 DO - https://doi.org/10.1364/OE.580984 SN - 1094-4087 VL - 33 IS - 26 SP - 54733 EP - 54746 PB - Optica Publishing Group AN - OPUS4-65263 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Agasty, Amit A1 - Costard, Rene A1 - Kadoke, Daniel A1 - Kind, Thomas A1 - Hicke, Konstantin A1 - Hüsken, Götz T1 - A comprehensive experimental assessment of reinforced concrete walls under blast: In situ monitoring of loading, dynamic response and damage with NDT methods and embedded sensors N2 - A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of resource intensive replication tests. For an efficient assessment without performing full scale blast tests, e.g., supported by numerical simulations, detailed knowledge is necessary to predict the blast loading from a given charge configuration, the resulting dynamic response of the structure under investigation as well as the resulting damage. Validation of numerical simulation requires the spatially resolved acquisition of all these parameters in real time. In this paper we present a set of measurement techniques and discuss their suitability for monitoring reinforced concrete (RC) walls under blast loading. Different blast-loading scenarios were realized by varying the charge weight and the standoff distance. The dynamic loading of the wall was characterized with pressure sensors complemented by numerical simulations using the APOLLO Blastsimulator and ConWep. High speed digital image correlation (DIC) was implemented in combination with multiple acceleration sensors to observe the dynamic deflection of the walls during the loading and to determine the residual deformation after the loading had ceased. In addition, one test specimen was instrumented with fiber optic sensor cables. These fiber optic sensors were used for distributed acoustic sensing (DAS) delivering information on dynamics of compression and tension cycles from within the structure. Additionally, the local damage pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) to enable the characterization of visual and non-visual damage to the structure. The obtained information was compared to results by an ultrasound structure-scanner. KW - Blast testing KW - Reinforced concrete KW - NDT methods KW - Fiber optic sensing KW - DIC KW - Numerical simulations KW - APOLLO Blastsimulator PY - 2025 DO - https://doi.org/10.1177/20414196251353795 SN - 2041-4196 SP - 1 EP - 31 PB - Sage Publications AN - OPUS4-63832 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Johann, Sergej A1 - Baensch, Franziska A1 - Sturm, Patrick A1 - Tiebe, Carlo A1 - Pötschke, Samuel A1 - Lay, Vera ED - Holl, H. T1 - HF RFID-based measurement comparison for method optimization in M2 concrete and alkali-activated mortars N2 - Monitoring of repositories for radioactive waste requires techniques which can be applied long-term under harsh conditions. In this work, the reliability and suitability of materials and a capacitive sensor for measuring relative humidity are investigated, which are to be embedded in the special concrete components for the closure structures of underground repository. Preliminary tests with accelerated aging of the materials used, validation of the sensors under difficult conditions, investigations of the surfaces after aging by pH 14 solution, defined exposure of the sensors in a climatic chamber and the microscope images are discussed. The results will be used for further development and optimization of the RFID based sensor systems which can be applied to monitor the condition of different building structures without cabling. T2 - 37th Danubia - Adria Symposium on Advances in Experimental Mechanics CY - Linz, Austria DA - 21.09.2021 KW - Alkali-activated mortars KW - Passive sensor interface KW - RFID KW - Structural health monitoring KW - Smart structures PY - 2022 DO - https://doi.org/10.1016/j.matpr.2022.03.465 SN - 2214-7853 VL - 62 IS - 5 SP - 2571 EP - 2576 PB - Elsevier CY - Amsterdam AN - OPUS4-54697 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -