8.6 Faseroptische Sensorik
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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 The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement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.
We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements.
Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels
(2024)
We report on the development and testing of 70 MPa hydrogen pressure
vessels with integrated fiber optic sensing fibers for automotive use. The paper deals with the condition monitoring of such composite pressure vessels (CPVs) using the optical backscatter reflectometry (OBR) applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the polymer liner during the manufacturing process of the carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors can be an opportunity for monitoring the material degradation of CPVs under static and cyclic loading.
Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels
(2024)
We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for automotive use. The paper deals with the condition monitoring of such composite pressure vessels (CPVs) using the optical backscatter reflectometry (OBR) applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the polymer liner during the manufacturing process of the carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors can be an opportunity for monitoring the material degradation of CPVs under static and cyclic loading.
We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements.
Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin.
Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing
(2024)
We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future.
Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin.
Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing
(2024)
We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future.
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.
Protection against terroristic or accidental scenarios in industrial settings requires suitable designs of structures to resist blast loads. Field testing as well as finite element simulations are among the techniques available to engineers in the understanding of the structural behavior against blast loading.
As blast testing of complex scenarios can be very resource intensive, tests are generally performed for simplified scenarios. Numerical tools can be used to model these scenarios to get better insight into blast loading, structural response, and the resulting damage to the structure. During the next steps, the simplified scenario is successively modified in numerical simulations to incorporate complexities that cannot be covered in blast testing experiments. One of the conditions for this approach to work is that the original simplified numerical simulation is valid. The scopes and challenges encountered in such a validation are the focus of this presentation/article.
A relatively ‘simple’ field test of a horizontal reinforced concrete (RC) slab subjected to blast loading is taken as an example for validation of the performance of numerical tools. The blast test incorporated various measurement techniques to quantify the blast load as well as the behavior of the RC slab. Blast load was measured using flush mounted piezoelectric pressure gauges, whereas acceleration sensors and fiber-optic sensor cables were used to characterize the dynamic behavior of the slab under blast loading. Additionally, damage characteristics were ascertained also using fiber optic sensing. The application of such measurement techniques, along with different numerical software available for the analysis of the scenario in question, demonstrate the scope of our contribution.
When it comes to the challenges, it begins with proper instrumentation of the test specimen followed by the data processing. For numerical modelling, geometric conditions with appropriate boundary constraints, physical conditions such as the configuration of the rebars, as well as material parameters add to this challenge. The issues of choosing appropriate material models and comparison of results with multiple software tools will be discussed. This discussion forms the basis for a coherent approach to technical-safety assessment of blast effects on structures in its broader sense.
Current capabilities for full-scale field testing are highly resource intensive. Reliable small-scale experiments are an effective alternative. Characterization of the dynamic response and damage of RC elements to scaled blast loads was investigated in scaled-down field experiments. Spatially resolved information on the dynamic structural response using distributed fiber optic acoustic sensing (DAS) and acceleration as well as blast loading by piezoelectric pressure sensors.
Wir präsentieren unsere Forschungsergebnisse zur Entwicklung eines faseroptischen Sensorsystems mit polymeroptischen Fasern integriert in Geokunststoffe für die Überwachung von geotechnischen Anlagen. Das Sensorsystem dient der räumlichen Auflösung lokaler Dehnungsereignisse im für Anwender signifikanten Größenordnungsbereich von 3 % bis 10 %. Das gesamte Sensorkonzept besteht aus einem praxistauglichen Messsystem basierend auf der digitalen inkohärenten optischen Frequenzbereichsreflektometrie (I-OFDR) und einer Sensormatte in Form eines Geotextils mit integrierten faseroptischen perfluorierten polymeroptischen Fasern (PF-POFs).
Distributed fiber optic sensors for structural health monitoring of composite pressure vessels
(2024)
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.
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.
Die Nutzung von Sensortechnologien, insbesondere im Bereich der Gasdetektion mit einem Schwerpunkt auf Wasserstoff, spielt eine entscheidende Rolle in verschiedenen Anwendungsbereichen der Wasserstofftechnologie. Sicherheitsüberwachung, Leckdetektion und Prozesskontrolle gehören zu den prominenten Anwendungsgebieten dieser Sensortechnologien. Ein zentrales Ziel ist die Erkennung von freigesetztem Wasserstoff sowie die genaue Bestimmung des Wasserstoff-Luftverhältnisses mithilfe von Gassensoren. Dies ist von entscheidender Bedeutung, um potenzielle Gefahren frühzeitig zu erkennen und angemessene Maßnahmen zu ergreifen.
Ein weiterer Schwerpunkt dieses Beitrags liegt auf der Analytik und der Verwendung zertifizierter Referenzmaterialien in Verbindung mit Metrologie für die Wasserstoffspeicherung. Dies gewährleistet eine präzise und zuverlässige Charakterisierung von Wasserstoff und unterstützt die Entwicklung sicherer Speichertechnologien. Im Rahmen des Euramet-Vorhabens Metrology for Advanced Hydrogen Storage Solutions (MefHySto) wird eine Kurzvorstellung präsentiert.
Der Vortrag stellt zwei zerstörungsfreie Prüfverfahren zum strukturellen Zustandsüberwachung (Structural Health Monitoring, SHM) für Wasserstofftechnologien vor. Insbesondere die Fehlstellenerkennung mittels geführter Ultraschallwellen spielt eine bedeutende Rolle bei der Lebensdauerüberwachung von Wasserstoffspeichern. Ein weiterer Aspekt ist die Anwendung faseroptischer Sensorik zur Schadensfrüherkennung von Wasserstoffspeichern. Diese zerstörungsfreien Prüfverfahren ermöglichen eine präzise und frühzeitige Identifizierung von Schäden, was die Sicherheit und Effizienz von Wasserstoffspeichersystemen entscheidend verbessert.
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.
A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of replication-tests. Such test results are necessary to clarify the causes within the scope of forensic investigations. To gain important insights into the behavior of structures and materials under such loading, field tests may also be performed in accordance with different test standards. To determine the resistance of building-structures after explosions, estimation of the residual load-bearing capacity in addition to the assessment of dynamic structural response and damage to the building components is important. In most cases an evaluation of structural integrity is based only on the visual damage, resulting in an overestimation of the residual capacity.
The Bundesanstalt für Materialforschung und -prüfung (BAM) operates the Test site for Technical Safety (TTS) on an area measuring about 12 km2 in the Federal State of Brandenburg for execution of true-to-scale explosion tests. At the TTS, building component testing was performed to assess the suitability of different non-destructive testing methods to characterize the dynamic structural response and damage resulting from the detonation of high explosives.
Different blast-loading scenarios were realized by varying the net explosive mass and the standoff distance with all scenarios representing a near-field detonation. The test object was a reinforced concrete wall 2 m high, 2.5 m wide and 20 cm thick, fixed at both vertical edges. The dynamic loading of the wall was characterized with 8 piezoelectric pressure sensors flush-mounted on the front surface, thus measuring the reflected pressures from the shock wave. The tests were conducted with the aim of characterizing the global behavior of the wall under dynamic shock loading and the resulting local damage pattern, respectively. High speed digital image correlation was implemented in combination with multiple acceleration sensors to observe the rear surface of the wall to chart the dynamic deflection 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, both fixed to the rebars and embedded in the concrete-matrix, respectively. Firstly, these sensors were interrogated during the blast test by a distributed acoustic sensing (DAS) device using a particularly high sampling rate to measure the shock-induced vibrations in the structure with high temporal resolution. This delivers information on dynamics of compression and tension cycles from within the structure. Secondly, the local damage-pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) by interrogating the embedded fiber optic sensors with a high spatial resolution DSS device after each blast. This enabled the characterization of non-visual damage to the structure, in particular with regard to the formation of localized cracks in the concrete matrix. The DSS was further complimented by a structure-scanner based on ultrasonic measurements.
Our contribution describes this new test approach in detail. Results of the three datasets, namely dynamic shock loading, global behavior of the test object and the local damage pattern will be presented. The suitability of the implemented measurement methods will be discussed in combination with the challenges in their application for technical safety evaluation of building components under explosive loading.
We present a prototype of a distributed POF sensing system for strain detection in road construction and civil engineering ready to be used in practical applications. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR). In this approach, the strain-induced changes in the Rayleigh backscattering profile along a perfluorinated polymer optical fiber (PF-POF) are determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit. The digital unit replaces an oversized vector network analyzer (VNA) enabling suitability of the measurement system for out-door use and providing a significant reduction of the total system costs at the same time. The entire sensor con-cept includes the use of geosynthetics with integrated PF-POFs as two-dimensional sensor structures for geotech-nical applications. The robustness and the functionality of the distributed POF I-OFDR sensors were proven in the field. The field test involved embedding of two geomats, each with two polymer optical sensor loops, into the embankment of the federal road B 91 in a section near Leipzig. The measurements conducted after the sensor-based geomats had been installed confirmed both the potential of such smart geosynthetics and the functionali-ty of the digital I-OFDR prototype.
Zur Bewertung der Widerstandsfähigkeit von Schutzobjekten und Bauteilen gegen Explosionsbeanspruchung führt die Bundesanstalt für Materialforschung und -prüfung (BAM) regelmäßig Sprengversuche im Realmaßstab auf einem Testgelände durch.
Solche Versuche sind planungs- und ressourcenintensiv, sodass sie zunehmend durch numerische Simulationen ergänzt bzw. ersetzt werden.
Aktuelle Forschungsarbeiten zielen darauf ab, neue Berechnungsmethoden und Materialmodelle zu entwickeln, mit denen die Bestimmung der Bauteilantwort sowie die daraus resultierende Schädigung nach einem Explosionsereignis möglich sind.
Zur Validierung solcher Ansätze ist es notwendig, systematische Versuche mit unterschiedlichen Szenarien durchzuführen, bei denen die relevanten Parameter mit Hilfe verschiedener Messmethoden aufgezeichnet werden.
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.
While hydrogen is one of the most promising energy carriers, the safety of hydrogen storage technology remains one of the most important factors for technological and societal approval. While the engineering safety factors of the pressure vessels are kept high, the periodic inspection and the limited lifetime are making the application very costly considering manpower, time, money, and material waste. The development of an integrated structural health monitoring system can allow an easy transition from the current situation to cost-effective predictive maintenance. Hence, we propose to integrate three different SHM systems into hydrogen pressure vessels, namely guided wave ultrasonics, acoustic emission, and fibre optic sensing, to continuously monitor the condition and integrity.
In this work, we evaluated the condition of a Type IV composite overwrapped pressure vessel using ultrasonic guided wave propagation. We mounted fifteen piezo-electric wafers on the composite cylinder by shaping three rings containing five sensors each. We acquired data from the sensor network following different boundary conditions with artificial damages on the selected locations. The data were evaluated with guided wave tomography techniques using ultrasonic features (amplitude, frequency, etc.) as well as artificial intelligence (AI). The results suggest that both traditional guided wave fusion techniques and AI-based characterization methods can detect artificial damages. In future work, it is planned to integrate acoustic emission and fibre optic sensing. Moreover, the measurement and the test results will be implemented into a digital twin to derive trends and make predictions on the damage propagation as well as the remaining useful lifetime.
This work has received funding from German Ministry of Economic Affairs and Climate Actions within the QI-Digital initiative (www.qi-digital.de).
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.
We present a temperature sensor based on a polymer exhibiting a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. Parameters are chosen such that the solution exhibits a cloud point in a temperature range of 30° C to 39°C. The characteristic of thermoresponsive polymers with an LCST, is that above that temperature phase separation of the polymer takes place which leads to a temperature-dependent formation of a cloudy suspension. An optical intensity measurement over the desired temperature range is established by an increase of optical attenuation inside the polymer solution caused by a rising temperature. For our purpose, the polymer capillary is connected to transmitter and receiver via a Polymer Optical Fibre (POF). Our intensity measurement is, to the best of our knowledge, a novel method and can be considered simple when compared to existing fibre-based temperature measurement techniques. Due to the lack of electrical components at the probe, this sensor is suitable for measurements in strong electromagnetic fields and environments for which flying sparks are hazardous, i.e., inflammable fluids or gases. Furthermore, all manufactured sensors share the same temperature dependence and, therefore, are well-suited for comparative measurement, e.g., flow measurement systems. With the given temperature range, a body temperature measurement is also suitable.
We present a high-precision fiber-optic temperature sensor based on a polymer possessing a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. For a temperature higher than the LCST the transparent homogenous polymer solution changes into a suspension which forms an opaque fluid. The resulting temperature-dependent turbidity influences the transmission characteristics measured using a simple fiber-optic system.
The presented development provides a small-sized, robust temperature sensor, patented by the BAM (patent nr.: EP 3 043 161 B1), easy to integrate in many application fields, especially for process monitoring.
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.
We report on the development of a field-proven distributed fiber optic sensing system for structural health monitoring in road construction and civil engineering. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR) for distributed strain detection along a polymer optical fiber. In this method, the strain-induced backscatter increase in a graded-index multimode perfluorinated polymer optical fiber (PF-POF) is determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit.
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.
Im vorliegenden Bericht wurde untersucht, Spanndrahtbrüche in einem Brückenträger sowohl mittels Schallemissionsanalyse (SEA) als auch mit (eingebetteter) verteilter faseroptischer akustischer Sensorik (DAS) zu erkennen. Die Ergebnisse zeigen, dass die Bruchsignale anhand beider Messverfahren detektiert werden können. Die jeweils detektierten Drahtbrüche werden miteinander verglichen. Durch eine Gegenüberstellung werden SEA und DAS detailliert dargestellt.
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.
Der Beitrag behandelt verschiedene Themen aus dem H2Safety@BAM-Kompetenzfeld Sensorik, Analytik und zertifizierte Referenzmaterialien (SensRef) mit Schwerpunkt auf Mess- und Prüfverfahren, die verschiedene Analyseverfahren, Sensortechnologien und Ultraschallwellen nutzen. Ein Teil davon ist das Euramet-Vorhaben "MefHySto" zur Metrologie der Wasserstoffspeicherung. Des Weiteren werden Verfahren zur Erkennung von freigesetztem Wasserstoff und zur Bestimmung des Wasserstoff-Luftverhältnisses mithilfe von Gassensoren behandelt. Ein weiterer Aspekt ist die zerstörungsfreie Fehlstellenerkennung mit einem integrierten Zustandsüberwachungssystem, das auf geführten Ultraschallwellen basiert und zur Überwachung der Lebensdauer von Composite-Behältern (Wasserstoffspeicher) dient. Zudem wird die Anwendung faseroptischer Sensorik zur Schadenfrüherkennung an Wasserstoffspeichern diskutiert. Dabei werden erkennbare Dehnungsänderungen an Druckbehältern als Indikatoren genutzt.
Introduction to group 8.6
(2023)
Sprengversuche sind ein wesentliches Werkzeug, um die Eignung von Schutzobjekten und Bauteilen zum Schutz der Bevölkerung vor Anschlägen mit explosionsgefährlichen Stoffen wie Explosivstoffen, sogenannten „home-made-explosives“ (HME) und unkonventionellen Spreng- und Brandvorrichtungen (USBV) zu bewerten. Für forensische Zwecke werden oft Explosionsszenarien nachgebildet, um die Auswirkung von Anschlägen oder Unfällen genau untersuchen zu können. Feldversuche in Kombination mit numerischen Simulationen ermöglichen eine Charakterisierung der Belastung und Schädigung von Bauteilen und Strukturen unter hochdynamischen Explosionsbelastungen.
Auf dem Testgelände Technische Sicherheit (TTS) der BAM in Horstwalde, Brandenburg werden Sprengversuche im Real-Maßstab durchgeführt. Dabei werden Bauteile mit Messtechnik ausgestattet, um eine dynamische Strukturcharakterisierung mittels sensorbasierten Monitorings zu ermöglichen.
In diesem Vortrag werden am Beispiel einer Versuchskampagne mit einer Stahlbetonwand als Prüfkörper die Eignung verschiedener Prüfmethoden bei Sprengversuchen evaluiert. Es werden Messmethoden zur Charakterisierung der Explosionsereignisse, des globalen Strukturverhaltens sowie der lokalen Schädigung vorgestellt.
Die direkte Charakterisierung der Explosionsbelastung erfolgt mit Hilfe von piezo-elektrischen Druckaufnehmern (bündig mit der Oberfläche im Prüfkörper montiert) und High-Speed-Kameras. Die Beobachtung der explosionsinduzierten Reaktion des Bauteils während und nach dem Eintreffen der Stoßwelle erfolgt mittels Beschleunigungssensoren, Stereo-Fotogrammetrie sowie mit in den Prüfkörper eingebetteter verteilter faseroptischer Sensoren, mit Hilfe derer die Dehnungsraten im Bauteil zeit- und ortsaufgelöst gemessen werden können. Die faseroptischen Sensoren erlauben außerdem die örtlich hochaufgelöste Bestimmung statischer Dehnungsänderungen im Bauteil nach einem Versuch, sodass auch nicht sichtbare Schädigungen wie kleinste Risse detektiert und charakterisiert werden können. Vergleichende Messungen mit Ultraschall komplettieren die Schadenscharakterisierung. In unserem Beitrag diskutieren wir die Herausforderungen dieser Messungen sowie die Ergebnisse und Möglichkeiten der genannten komplementären Messmethoden.
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.
After describing the basics of distributed fiber optic sensing in general and distributed fiber optic acoustic sensing (DAS) in particular, several currently and previously investigated fields of application of DASin infrastructure monitoring and seismic monitoring are presented. A focus is placed on seismic measurements and DAS measurements determining the dynamic responses of concrete structures to certain stimuli.
Multi-sensory monitoring and ultrasound for quality assurance at underground sealing structures
(2023)
Within the safety concepts of underground disposal sites of nuclear waste, engineered barriers play an important role. As these sealing structures have high demands concerning integrity, we aim at advancing the available construction materials, monitoring, and inspection techniques within the project SealWasteSafe. A specifically developed alkali-activated material is compared to classical salt concrete. A comprehensive multi-sensory monitoring scheme is used at 150-340 l specimens to monitor setting and hardening of both materials. All sensors are demonstrated to resist the highly alkaline environments. Besides cabled and wireless temperature and humidity of the materials, strain variations using fibre optic sensors and acoustic emissions are recorded over periods of at least 28 days, partly for more than eight months. After hardening of the specimens, further nondestructive evaluations using ultrasonic echo and thermographic measurements are conducted.
Preliminary results proof the suitability of the tested sensors and clearly highlight differences between the tested materials. Particularly, the newly developed alkali-activated material shows lower acoustic emission activity indicating less cracking activity. Additionally, unique ultrasonic methods will enable better images of potential internal objects and cracks at in-situ sealing structures. A largescale ultrasonic system is optimised to reliably detect objects at a depth exceeding 9 m while still obtaining a good resolution. Modelling studies show the potential of further increasing the distance between individual transducer arrays. Additionally, a new ultrasonic borehole probe using phased arrays allowing for beam focussing is constructed and tested. Laboratory measurements at a halfcylindrical concrete specimen coincide well with the previous modelling. In total, the presented safe materials, detailed monitoring approaches and ultrasonic quality assurance methods will help to obtain safe sealing structures within salt as a host rock. The concepts can partly be transferred to sealing structures in alternative host rocks and will also be valuable for non-nuclear waste repositories.
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.
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.
Es wird ein faseroptisches Messsystem vorgestellt, welches auf Basis der Rayleigh-Rückstreumessung eine ortsauflösende Detektion von signifikant großen Dehnungen bei Bauvorhaben im von Anwendern geforderten Größenordnungsbereich von 3 % bis 10 % ermöglicht. Mit dem Verfahren der digitalen inkohärenten optischen Frequenzbereichsreflek-tometrie (I-OFDR) werden materialspezifische Vorteile robuster perfluorierter polymeroptischer Fasern (PF-POF) erschlossen. Für den angestrebten industriellen Einsatz für die Zustandsüberwachung im Bereich des Tunnel- und Spezialtiefbaus soll die spezifische Rückstreuzunahme in der PF-POF hochauflösend gemessen werden. In diesem
Vortrag wird gezeigt, dass sich in ersten Projektergebnissen das digitale I-OFDR für eine Dehnungsdetektion von bis zu 10% eignet.
In this presentation, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is shown. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is devel-oped for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and costeffective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber.
Der Beitrag beinhaltet Themen vom H2Safety@BAM-Kompetenzfeld Sensorik, Analytik und zertifizierte Referenzmaterialien (SensRef) mit Fokus auf Mess- und Prüfverfahren mit verschiedenen Sensortechnologien und Ultraschallwellen:
Metrologie zur Wasserstoffspeicherung - Euramet-Vorhaben "MefHySto",
Erkennung von freigesetztem Wasserstoff sowie die Bestimmung des Wasserstoff-Luftverhälntisses mit Gassensoren, zerstörungsfreie Fehlstellenerkennung mit integriertem Zustandsüberwachungssystem basierend auf geführten Ultraschallwellen zur Lebensdauerüberwachung von Composite-Behältern (Wasserstoffspeicher) sowie faseroptische Sensorik zur Schadenfrüherkennung von Wasserstoffspeichern aufgrund erkennbarer Dehnungsänderungen an Druckbehältern.
In this paper, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is presented. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is developed for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and cost-effective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber.
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.
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.
Monitoring of construction projects plays increasingly important role in the implementation of growing needs and requirements, especially in geologically difficult areas. This particularly concerns tunnelling and special civil engineering projects. The use of fiber optic sensors for structural health monitoring provides significant economic advantages regarding lower life-cycle costs of the civil infrastructure. We present our achievements in the development of distributed polymer optical fiber sensors based on Rayleigh and Brillouin scattering for early damage detection.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, a second data analysis approach based on artificial neural networks (ANN) was evaluated. Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
Chemical and pharmaceutical companies need to find new ways to survive successfully in a changing environment, while finding more flexible ways of product and process development to bring their products to market faster - especially high-value, high-end products such as fine chemicals or pharmaceuticals. This is complicated by changes in value chains along a potential circular economy.
One current approach is flexible and modular chemical production units that use multi-purpose equipment to produce various high-value products with short downtimes between campaigns and can shorten time-to-market for new products. Online NMR spectroscopy will play an important role for plant automation and quality control, as the method brings very high linearity, matrix independence and thus works almost calibration-free. Moreover, these properties ideally enable automated and machine-aided data analysis for the above-mentioned applications.
Using examples, this presentation will outline a possible more holistic approach to digitalization and the use of machine-based processes in the production of specialty chemicals and pharmaceuticals through the introduction of integrated and networked systems and processes.
Die B1 Brücke am Altstädter Bahnhof in Brandenburg an der Havel wurde im Dezember 2019 aufgrund von während einer turnusmäßigen Inspektion entdeckten Rissen und Hohlstellen entlang der vorgespannten Längsträger gesperrt und im Mai 2021 abgebrochen. In der Zwischenzeit wurde die Brücke detailliert überwacht. Vor dem Abriss wurden zudem umfangreiche Untersuchungen zur Bestandsaufnahme und Schadensanalyse sowie Tests moderner Sensorik vorgenommen. Dabei konnte sowohl die richtige, zuvor nicht sicher bekannte Anzahl von Spanngliedern in den Querträgern sicher ermittelt werden als auch durch moderne Varianten der Schwingungsmessungen und der faseroptischen Sensorik zusätzliche Kenntnisse püber das Bauwerksverhalten ermittelt werden . In dem Beitrag werden die Verfahren mit ihren Möglichkeiten und Grenzen vorgestellt, die Ergebnissee an der Brücke in Brandenburg erläutert und zukünftige Einsatzmöglichkeiten diskutiert.
This contribution introduces an investigation of a large-scale prestressed concrete bridge model (“BLEIB” structure at the BAM-TTS open air test site) by means of on-site cooperative measurements. This bridge has an external post-tensioning system and has been instrumented with the ultrasonic transducers, temperature sensors and optical fibers for Distributed Acoustic Sensing (DAS). Our experiment was designed to test the suitability of the novel 6C sensors developed within the framework of the GIOTTO project – the IMU50. The IMU50 sensor enables vibration measurements in translation along three axes and rotation around three axes. The geophone sensors were considered for complementary measurements of vertical velocity response. In the experiment, several perturbations were achieved by controlling the external influence factors such as loading and prestressing changes. The aim of the integrated measurement strategy was to fully observe the results of the condition change and to verify the effectiveness of multiple sensors for bridge monitoring.
Die B1 Brücke am Altstädter Bahnhof in Brandenburg an der Havel wurde im Dezember 2019 aufgrund von während einer turnusmäßigen Inspektion entdeckten Rissen und Hohlstellen entlang der vorgespannten Längsträger gesperrt und im Mai 2021 abgebrochen. In der Zwischenzeit wurde die Brücke detailliert überwacht. Vor dem Abriss wurden zudem umfangreiche Untersuchungen zur Bestandsaufnahme und Schadensanalyse sowie Tests moderner Sensorik vorgenommen. Dabei konnte sowohl die richtige, zuvor nicht sicher bekannte Anzahl von Spanngliedern in den Querträgern sicher ermittelt werden als auch durch moderne Varianten der Schwingungsmessungen und der faseroptischen Sensorik zusätzliche Kenntnisse püber das Bauwerksverhalten ermittelt werden . In dem Beitrag werden die Verfahren mit ihren Möglichkeiten und Grenzen vorgestellt, die Ergebnissee an der Brücke in Brandenburg erläutert und zukünftige Einsatzmöglichkeiten diskutiert.
This contribution introduces an investigation of a large-scale prestressed concrete bridge model (“BLEIB” structure at the BAM-TTS open air test site) by means of on-site cooperative measurements. This bridge has an external post-tensioning system and has been instrumented with the ultrasonic transducers, temperature sensors and optical fibers for Distributed Acoustic Sensing (DAS). Our experiment was designed to test the suitability of the novel 6C sensors developed within the framework of the GIOTTO project – the IMU50. The IMU50 sensor enables vibration measurements in translation along three axes and rotation around three axes. The geophone sensors were considered for complementary measurements of vertical velocity response. In the experiment, several perturbations were achieved by controlling the external influence factors such as loading and prestressing changes. The aim of the integrated measurement strategy was to fully observe the results of the condition change and to verify the effectiveness of multiple sensors for bridge monitoring.
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.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.
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.
Es wird ein faseroptisches Messsystem vorgestellt, welches auf Basis der Rayleigh-Rückstreumessung eine ortsauflösende Detektion von signifikant großen Dehnungen bei Bauvorhaben im von Anwendern geforderten Größenordnungsbereich von 3 % bis 10 % ermöglicht. Mit dem Verfahren der digitalen inkohärenten optischen Frequenzbereichsreflektometrie (I-OFDR) werden materialspezifische Vorteile robuster perfluorierter polymeroptischer Fasern (PF-POF) erschlossen. Für den angestrebten industriellen Einsatz für die Zustandsüberwachung im Bereich des Tunnel- und Spezialtiefbaus soll die spezifische Rückstreuzunahme in der PF-POF hochauflösend gemessen werden. Diese Veröffentlichung zeigt in ersten Projektergebnissen die Eignung des digitalen I-OFDR für eine Dehnungsdetektion von bis zu 10%.
Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring
(2022)
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).
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.
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.
Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach
(2022)
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.
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.
Die verteilte faseroptische akustische Sensorik (DAS) wird vorgestellt, hinsichtlich ihrer Leistungsfähigkeit beschrieben und verschiedenste Anwendungsfelder, z.B. für das Zustandsmonitoring oder für seismische Messungen, beispielhaft gezeigt. Zudem werden mögliche Anwendungsfelder von DAS für den Kontext Kerntechnische Entsorgung (KTE) und Rückbau aufgezeigt.
Intelligent sensor systems, certified reference materials and instrumental analytical-chemical methods contribute to safety and functionality in hydrogen technologies.
This article gives a brief overview of SensRef activities in the Competence Centre H2Safety@BAM on the issues: Analytical methods for the determination of hydrogen purity, certified reference materials as measurement standards with regard to gas quality (primary calibrators) of BAM, test methods for gas sensor systems to detect hydrogen in air as well as the application of fibre-optic sensor systems to monitor the expansion and ageing behaviour of composite containers in hydrogen technologies.
Der Vortrag zum Mess- und Prüfverfahren mit verschiedenen Sensortechnologien und Ultraschallwellen beinhaltet die Themen:
Metrologie zur Wasserstoffspeicherung - Euramet-Vorhaben "MefHySto",
Erkennung von freigesetztem Wasserstoff sowie die Bestimmung des Wasserstoff-Luftverhälntisses mit Gassensoren, zerstörungsfreie Fehlstellenerkennung mit integriertem Zustandsüberwachungssystem basierend auf geführten Ultraschallwellen zur Lebensdauerüberwachung von Composite-Behältern (Wasserstoffspeicher) sowie faseroptische Sensorik zur Schadenfrüherkennung von Wasserstoffspeichern aufgrund erkennbarer Dehnungsänderungen an Druckbehältern.
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.
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.
In this work, we present our results achieved in several research activities for development of distributed fiber optic radiation sensors using glass and polymer optical fibers. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accompanying change in the refractive index of the fiber core can be used for distributed radiation monitoring.
The topic of the presentation are distributed fiber optic measurement techniques that can be used for long-term monitoring of strain, temperature and radiation distribution in the radiation environment. The fiber optic sensors applied to the surface of waste packages provide detection and localization of cracks.
In this work, we present our results achieved in several research activities for development of fiber optic dosimeters. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accom panying change in the refractive index of the fiber core can be used for distributed radiation monitoring in the kGy and MGy range, respectively. Depending on the fiber type and material the RIA shows varying response to dose rates, environmental temperatures and the wavelength of the laser source used.
The internal project "SealWasteSafe" of the Federal Institute for Materials Research and Testing is dedicated to the further improvement in the construction of sealing systems for underground repositories of radioactive waste. Particularly, a multi-sensory concept is developed to ensure quality assurance and continuous long-term monitoring on the engineered barriers. Therefore, beside other measures, the temperature and equilibrium moisture of the construction shall be monitored and respective multi-sensoric monitoring concepts are tested first on the laboratory scale. One focus in this project is on embedded application in alkali-activated mortars (AAM), which results in special requirements for the resistance of the sensors to strongly alkaline environments with up to pH 14. In addition, the ingress of water along cables or at sensor positions is critical for sensors embedded in concrete, as the cable access of the sensor housing is a major weak point. It is therefore advisable to equip wireless high frequent radio frequency identification-based sensors, so called HF RFID sensors (short: RFID), that do not require cable access and thus have a stable and leak-proof sensor housing for long-term monitoring. In this contribution, the results from temperature, moisture and transmitted power monitoring of a AAM and a M2 specimens by means of HF RFID sensor systems are presented.
The internal project "SealWasteSafe" of the Federal Institute for Materials Research and Testing is dedicated to the further improvement in the construction of sealing systems for underground repositories of radioactive waste. Particularly, a multi-sensory concept is developed to ensure quality assurance and continuous long-term monitoring on the engineered barriers. Therefore, beside other measures, the temperature and equilibrium moisture of the construction shall be monitored and respective multi-sensoric monitoring concepts are tested first on the laboratory scale. One focus in this project is on embedded application in alkali-activated mortars (AAM), which results in special requirements for the resistance of the sensors to strongly alkaline environments with up to pH 14. In addition, the ingress of water along cables or at sensor positions is critical for sensors embedded in concrete, as the cable access of the sensor housing is a major weak point. It is therefore advisable to equip wireless high frequent radio frequency identification-based sensors, so called HF RFID sensors (short: RFID), that do not require cable access and thus have a stable and leak-proof sensor housing for long-term monitoring. In this contribution, the results from temperature, moisture and transmitted power monitoring of a AAM and a M2 specimens by means of HF RFID sensor systems are presented.
Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
(2021)
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.