TY - CONF A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Dual functionality of wavelength scanning coherent optical time domain reflectometer N2 - Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system. T2 - European Workshop on Optical Fibre Sensors (EWOFS 2023) CY - Mons, Belgium DA - 23.05.2023 KW - Structural health monitoring KW - Distributed fiber sensing KW - Acoustic sensing PY - 2023 DO - https://doi.org/10.1117/12.2678098 VL - 12643 SP - 1 EP - 4 PB - SPEI AN - OPUS4-57590 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Dual-functionality of WS-COTDR for ground movement monitoring N2 - Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system. T2 - European Workshop on Optical Fibre Sensors (EWOFS 2023) CY - Mons, Belgium DA - 23.05.2023 KW - Distributed fiber sensing KW - Acoustic sensing KW - Structural health monitoring PY - 2023 AN - OPUS4-57591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shaheen, Sabahat A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - phi-g OTDR utilizing geometric phase N2 - Geometric phase measured per beat period in a ϕ-OTDR based on coherent heterodyne detection is used to measure strain. Proposed method is robust to polarisation mismatch fading as a polarisation mismatch between interfering beams is not a hindrance to the measurement of the geometric phase. The Geometric phase is a function of the intensities of the interfering beams as well as the envelope of the beat signal. Its calculation does not require phase unwrapping and accordingly does not suffer the phase unwrapping errors. It is required to be equated with the traditionally measured phase by applying a scaling factor. The spatial resolution of the measured strain is reduced as it is calculated per beat period. Results are verified using a piezo-electric transducer inline a fiber-under-test. T2 - European Workshop on Optical Fibre Sensors (EWOFS 2023) CY - Mons, Belgium DA - 23.05.23 KW - Coherent Heterodyne KW - Geometric Phase KW - Distributed Fiber Optic Sensor PY - 2023 DO - https://doi.org/10.1117/12.2678295 SP - 1 EP - 5 PB - SPIE AN - OPUS4-57551 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 - CONF A1 - Shaheen, Sabahat A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Earth’s near-surface characterisation using phi-OTDR based on geometric phase N2 - Phase-sensitive OTDR based on geometric phase for the first time detects surface waves generated by quarry blasts of 25 kg explosives. Inversion of dispersion curves provides depth profiles of shear-wave velocities of surface waves. T2 - Optica Sensing Congress CY - Munich, Germany DA - 30.07.2023 KW - Geophysics KW - Geometric Phase KW - Distributed Fiber Optic Sensor KW - Coherent Heterodyne KW - Surface waves PY - 2023 SP - 1 EP - 2 AN - OPUS4-58044 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 - Lu, Xin A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Approaching distributed ground motion sensing at high and low frequency ranges N2 - The phase and reflection spectrum of a standard distributed acoustic sensor based on an interferometer are used for fast and slow dynamic measurements, respectively, enabling the system to simultaneously measure various types of ground movement. T2 - 28th International Conference on Optical Fiber Sensors (OFS-28) CY - Hamamatsu, Japan DA - 20.11.2023 KW - Distributed acoustic sensing KW - Ground movement monitoring KW - Geomonitoring KW - Interferometer-based KW - DAS PY - 2023 SP - 1 EP - 4 PB - Optica CY - Washington D.C., USA AN - OPUS4-59098 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin A1 - Lu, Xin A1 - Chruscicki, Sebastian A1 - Krebber, Katerina T1 - Measurement of Slowly Changing and Quasi-Static Strain Signals for Ground Motion Monitoring Applications using Distributed Acoustic Sensing N2 - The suitability of wavelength-scanning COTDR for distributed dynamic strain sensing (DAS) along buried fiber cables for long-term geotechnical monitoring applications is demonstrated by experiments showing the method’s capability to demodulate slowly varying and quasi-static signals. T2 - Optica Sensing Congress 2024 CY - Toulouse, France DA - 15.07.2024 KW - Ground motion KW - Ground movement monitoring KW - Distributed fiber optic sensing KW - Quasi-static signals KW - Geomonitoring PY - 2024 SP - 1 EP - 2 PB - Optica CY - Washington D.C., USA AN - OPUS4-60816 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 working principle 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 - Underground gas storage KW - Distributed fiber sensing KW - Geothermal borehole KW - Ground movement KW - Leakage detection KW - Structure health monitoring PY - 2024 AN - OPUS4-60146 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 - 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 - 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 - Hicke, Konstantin A1 - Krebber, Katerina ED - Chung, Y. ED - Jin, W. ED - Lee, B. ED - Canning, J. ED - Nakamura, K. ED - Yuan, L. T1 - Towards efficient real-time submarine power cable monitoring using distributed fibre optic acoustic sensors N2 - Online condition monitoring of submarine power cables helps to avert failures and damages produced by mechanical impacts. We report, to our knowledge for the first time, on investigations regarding the feasibility of distributed fiber optic acoustic sensors based on C-OTDR, with the sensor fibres being embedded in the cable, to detect vibrations due to mechanical disturbances along the cable. We present first results of sensing experiments where acoustic signals are transmitted through water to simulate the corresponding submarine conditions. Furthermore, we show results evaluating the usefulness of fibre commonly embedded in existing power cable designs for our sensing purposes. T2 - 25th International Conference on Optical Fiber Sensors CY - Jeju-City, Jeju, Republic of Korea DA - 24.04.2017 KW - Distributed acoustic sensing KW - Power cable monitoring KW - C-OTDR KW - Fibre optic sensors KW - DAS PY - 2017 SN - 978-1-5090-4850-2 DO - https://doi.org/10.1117/12.2267474 SN - 0277-786X SN - 1996-756X VL - 10323 SP - 1032390-1 EP - 1032390-4 PB - SPIE CY - Bellingham, WA, USA AN - OPUS4-40088 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin A1 - Hussels, Maria-Teresa A1 - Eisermann, René A1 - Chruscicki, Sebastian A1 - Krebber, Katerina ED - Chung, Y. ED - Jin, W. ED - Lee, B. ED - Canning, J. ED - Nakamura, K. ED - Yuan, L. T1 - Condition monitoring of industrial infrastructures using distributed fibre optic acoustic sensors N2 - Distributed fibre optic acoustic sensing (DAS) can serve as an excellent tool for real-time condition Monitoring of a variety of industrial and civil infrastructures. In this paper, we portray a subset of our current Research activities investigating the usability of DAS based on coherent optical time-domain reflectometry (C-OTDR) for innovative and demanding condition Monitoring applications. Specifically, our application-oriented Research presented here aims at acoustic and vibrational condition monitoring of pipelines and piping systems, of rollers in industrial heavy-duty conveyor belt systems and of extensive submarine power cable installations, respectively. T2 - 25th International Conference on Fiber Optic Sensors CY - Jeju-City, Jeju, Republic of Korea DA - 24.04.2017 KW - Condition monitoring KW - Distributed fibre optic acoustic sensing KW - Fibre optic sensors KW - Industrial monitoring KW - DAS PY - 2017 SN - 978-1-5090-4850-2 DO - https://doi.org/10.1117/12.2272463 SN - 0277-786X SN - 1996-756X VL - 10323 SP - 103230J-1 EP - 103230J-4 PB - SPIE CY - Bellingham, WA, USA AN - OPUS4-40091 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin A1 - Hussels, Maria-Teresa A1 - Eisermann, René A1 - Chruscicki, Sebastian A1 - Krebber, Katerina T1 - Distributed Fibre Optic Acoustic and Vibration Sensors for Industrial Monitoring Applications N2 - We investigate the usability of distributed fibre optic acoustic sensing (DAS) for innovative and advanced monitoring applications in industrial and civil infrastructure installations. In this paper, we report on our ongoing application-oriented research activities regarding the utilization of DAS based on coherent optical time-domain reflectometry (C-OTDR) for condition monitoring of a variety of infrastructures. Specifically, our research presented here aims at acoustic condition monitoring of and fault detection in pipelines and industrial piping systems, at acoustic condition monitoring of rollers in industrial conveyor belt installations, and at acoustic condition monitoring of and threat detection in extensive submarine power cables, respectively. Furthermore, we show a method to mitigate the effect of sensitivity fading of C-OTDR based DAS due to unstable environmental conditions via the modification of the sensor fibre. This can help to provide a continuous adequate sensor functionality for a number of different industrial monitoring applications. T2 - SENSOR 2017 - 18th International Conference on Sensors and Measurement Technology CY - Nuremberg, Germany DA - 30.05.2017 KW - distributed acoustic sensing KW - fibre optic sensors KW - condition monitoring KW - industrial monitoring KW - C-OTDR PY - 2017 SN - 978-3-9816876-4-4 DO - https://doi.org/10.5162/sensor2017/B7.3 SP - 274 EP - 279 PB - AMA Service GmbH CY - Wunstorf AN - OPUS4-40473 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 - Stajanca, Pavol A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Distributed Fiberoptic Sensor for Simultaneous Humidity and Temperature Monitoring Based on Polyimide-Coated Optical Fibers N2 - Along temperature, humidity is one of the principal environmental factors that plays an important role in various application areas. Presented work investigates possibility of distributed fiberoptic humidity monitoring based on humidity-induced strain measurement in polyimide (PI)-coated optical fibers. Characterization of relative humidity (RH) and temperature response of four different commercial PI- and one acrylate-coated fiber was performed using optical backscattering reflectometry (OBR). The study addresses issues of temperature-humidity cross sensitivity, fiber response stability, repeatability, and the influence of annealing. Acrylate-coated fiber exhibited rather unfavorable nonlinear RH response with strong temperature dependence, which makes it unsuitable for humidity sensing applications. On the other hand, humidity response of PI-coated fibers showed good linearity with fiber sensitivity slightly decreasing at rising temperatures. In the tested range, temperature sensitivity of the fibers remained humidity independent. Thermal annealing was shown to considerably improve and stabilize fiber RH response. Based on performed analysis, a 20 m sensor using the optimal PI-coated fibers was proposed and constructed. The sensor uses dual sensing fiber configuration for mutual decoupling and simultaneous measurement of temperature and RH variations. Using OBR, distributed dual temperature-RH monitoring with cm spatial resolution was demonstrated for the first time. KW - Distributed humidity sensing KW - Fiberoptic sensors KW - Polyimide-coated optical fibers KW - Optical frequency-domain reflectometry KW - Dual sensing PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498871 DO - https://doi.org/10.3390/s19235279 VL - 19 IS - 23 SP - 5279 PB - MDPI AN - OPUS4-49887 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin A1 - Chruscicki, Sebastian A1 - Krebber, Katerina ED - Kalli, K. ED - O'Keeffe, S. O. ED - Brambilla, G. T1 - Real-time distributed temperature gradient sensing using amplitude-based C-OTDR and sensing fiber with inscribed scattering dots N2 - We present a technique for distributed temperature gradient sensing in real-time along an optical fiber utilizing simple amplitude-based direct-detection coherent optical time domain reflectometry (C-OTDR) and a special sensing fiber. Our technique enables us to determine phase changes or low-frequency variations of the C-OTDR signal stemming from temperature variations. The distinct feature of the used sensing fiber is its structuring with equidistant strongly scattering dots. Consecutive pairs of these scatterers form the dominant local interferometers, effectively overwriting the otherwise highly nonlinear transfer function of common optical fiber. This enables a quasi-phase-resolved evaluation of perturbation responses originating from temperature changes at sensor positions between the scatterers. Using our method, we show the measurement of a nonlinear temperature transient from a heating process with a maximum temperature gradient of 0.8 °C over 20 s and a total temperature increase of 28.4 °C. This method requires almost no post-processing and can be used for simultaneous distributed vibration sensing (DVS) and quantification of local temperature gradients in a single fiber, e.g., for the use in condition monitoring of infrastructure or industrial installations. T2 - Seventh European Workshop on Optical Fibre Sensors, 2019 CY - Limassol, Cyprus DA - 01.10.2019 KW - C-OTDR KW - DVS KW - Distributed vibration sensing KW - Distributed temperature gradient sensing KW - Fibre optic sensors KW - Structured fiber KW - Power cable monitoring PY - 2019 SN - 978-1-51063-123-6 DO - https://doi.org/10.1117/12.2539925 VL - 11199 SP - 1119920-1 EP - 1119920-4 PB - SPIE CY - Bellingham, WA, USA AN - OPUS4-49926 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -