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Organisationseinheit der BAM
- 8.6 Faseroptische Sensorik (170) (entfernen)
We demonstrate (and are the first to do so) 63 km Brillouin Optical Frequency-Domain Analysis (BOFDA) for temperature and strain monitoring using a 100 km fiber loop. The use of BOFDA for long-range applications can be considered a novel approach, as previous investigations focused on the utilization of Brillouin Optical Time-Domain Reflectometry and Analysis (BOTDR and BOTDA, respectively). At 51.7 km, a 100 m hotspot (37 °C) was detected without using distributed Raman amplification or image processing.
We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 °C have been measured along 75 km sensing fiber. A temperature change of 30 °C has been detected at 99.5 km.
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 demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment.
The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt.
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.
Acoustic emission testing
(2018)
The improvement potential of ultrafast all-optical switching by soliton self-trapping, using all-solid dual-core fibres with high index contrast, was analyzed numerically. The study of the femtosecond nonlinear propagation was performed based on coupled generalised nonlinear Schrödinger equations considering three fibre architectures: homogeneous cladding all-solid, photonic crystal air-glass, and photonic crystal all-solid. The structural geometries of all three architectures were optimised in order to support high-contrast switching performance in the C-band, considering pulse widths at the 100 fs level. Comparing the three structural alternatives, the lowest switching energies at common excitation parameters (1700 nm and 70 fs pulses) were predicted for the homogeneous cladding dual-core structure. Further optimization of the excitation wavelength and pulse width resulted in lower switching energies and simultaneous improvement of the switching contrasts at the combination of 1500 nm, 75 fs pulses and a fibre length of 43 mm. The spectral aspect in this optimised case expresses a broadband and uniform switching character with a span of over 200 nm and a contrast exceeding 30 dB at more frequency channels.
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).
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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are 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 developed 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-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible 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.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are 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 developed 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-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible 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.
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.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
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.
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.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.
Phase-sensitive optical time domain reflectometry (φOTDR) is an excellent distributed fiber sensing technique and has been applied in various areas. Its noise is however never been comprehensively studied to the best of our knowledge. The different detection noise sources in such a sensing system are thoroughly investigated. The impacts of thermal noise, shot noise and the beat between signal and the amplified spontaneous emission from a pre-amplifier have been theoretically and experimentally demonstrated. Due to the random nature of the φOTDR signal, the detection noise demonstrates distinct features at different fiber positions in a single measurement. The theoretical analysis and the experimental result explicitly affirm most of the fiber sections, and the difference at some positions may be explained by ambient noise.
We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single ug were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring Approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.
One of the biggest problems in high-voltage silicone rubber insulation cable accessories is the damage on electrical treeing, initiated by partial discharges. The electrical treeing starts at unavoidable imperfections inside the material or at interfaces. The damage is usually irreversible and leads around the starting points to a partial destruction of the material. To prolong the lifetime and thereby to increase the assurance of the structural component commonly, for mechanical improvement constituted filler is modified to obtain a self-healing silicone rubber. Damage mechanism is analysed to address the filler to the damage mode.
We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach.
This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest.
A Kramers-Kronig (KK) receiver is applied to a phase-sensitive optical time domain reflectometry based on direct detection. An imbalanced Mach-Zehnder interferometer with a 2× 2 coupler is used in sensing system to encode the phase information into optical intensity. The directly obtained signal is treated as the in-phase component, and the KK receiver provides the quadrature component by Hilbert transform of the obtained signal, so that the optical phase can be retrieved by IQ demodulation. The working principle is well explained, and the obtained phase variance is theoretically analyzed. The experiment demonstrates the functionality of the sensor and validates the theoretical analysis.
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.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
The substantial progresses in fiber-optic communications in combination with the increasing economic and political interest in structural health monitoring have led to a commercial establishment of distributed Brillouin sensing. The sensor systems, mainly based on the time-domain techniques, have been successfully implemented in the areas such as pipeline leak detection, geohazard effects, and ground movement detection.
This chapter introduces a further advancement in the area of Brillouin sensing in the frequency domain. The so-called Brillouin optical frequency-domain analysis (BOFDA) offers crucial perspectives in terms of dynamic range and cost efficiency.
The main principle of the frequency-domain approach takes advantage of the reversibility between the time and frequency domain given by a Fourier transform in the analysis of linear systems. The hereby presented overview gives a summary of the benefits and challenges of frequency-domain measurements closely tied to the narrowband recording of the complex transfer function. This function relates the counterpropagating pump and probe laser light along the sensor fiber providing the pulse response of the measurement system by applying the inverse Fourier transform (IFT). The strain or temperature distribution can be then determined from the retrieved Brillouin frequency shift (BFS) profile along the fiber.
In this thesis, a distributed Brillouin sensor in perfluorinated polymer optical fibers utilizing BOFDA is presented. These commercially available polymer fibers offer beneficial characteristics for sensing applications such as higher break down strain up to 100 %, minimal bending radii below 2 mm, higher sensitivity to temperature and lower sensitivity to strain compared to their silica equivalent.
The chosen wavelength of operation at 1319 nm corresponds to lower fiber propagation loss (< 37 dB/km) compared to other approaches at 1550 nm (150 - 250 dB/km). A 86 m PFGI-POF was successfully measured by BOFDA with spatial resolution of 3.4 m.
The findings related to humidity influences can serve as a basis for future distributed humidity sensors not only limited to stimulated Brillouin backscattering.
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.
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.
Division 8.6's competencies and work focus areas are shortly outlined and investigations related to pipeline and submarine power cable monitoring using different distributed fiber optic sensing techniques are presented with some results. Furthermore, BAM 8.6 expertise in the field of sensor application, embedding and integration is shown.
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.
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.
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.
Distributed Fibre Optic Sensing for Monitoring and Testing of Industrial and Civil Infrastructures
(2018)
Fibre optic sensors enable the gathering of sensory information in environments where conventional sensors fail or simply can not be utilised. Their small size enables their use in confined spaces as well as their embedding or integration in components, structures or composite materials while offering, depending on the approach, high sensitivity, very high spatial resolution and, especially, the possibility to detect or measure physical quantities in a distributed way, meaning spatially continuous sensing over very long distances.
Those distributed fibre optic sensors (DFOS) provide access to sensing information from different locations along the sensing fibre without the need for technologically challenging multiplexing of many point sensors and the subsequent sensor data fusion. In this way, DFOS are excellently suited as smart sensors for monitoring or testing purposes as the spatially distributed sensing data make available spatially resolved measurand profiles without a gap. Furthermore, the possible embedding or integration of DFOS could facilitate the development of smart structures or smart components. In particular distributed vibration sensing (DVS) and distributed temperature sensing (DTS) are also ideally suited for condition monitoring of machinery, industrial installations or extensive infrastructure, also, on-line real-time assembly or component testing in production lines, e.g. for quality control, is possible.
In this talk, we will shortly outline the underlying principles of some fibre optic sensing approaches, explain their fundamental differences in mode of operation and performance, and discuss their applicability and limitations. Moreover, we will report on a subset of current and previous applied research projects at BAM, investigating different areas of use for distributed fibre optic sensors in particular.
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.
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.
The distributed measurement of relative humidity is a sought-after capability for a wide range of applications in civil engineering and structural health monitoring. We show that polymethyl methacrylate (PMMA) optical fi-bers can be employed as a sensor medium to conduct distributed humidity measurement by analyzing Rayleigh backscattering traces obtained by OTDR. We make use of the effect that water penetrates the fiber core and directly influences the local fiber attenuation and Rayleigh backscatter coefficient. We conducted distributed backscattering analysis for two different pulse wavelengths: 500 nm and 650 nm. The 650 nm results are susceptible to both, attenuation changes and backscatter changes, whereas backscatter results at 500 nm are not affected by humidity-induced attenuation and only exhibit a change of Rayleigh backscattered power as a function of humidity. The combined measurement and analysis of both parameters at these two wavelengths has the advantage that cross-sensitivities on backscatter change and attenuation, such as strain and tempera-ture changes, could be separated from the humidity response of the fiber. We present laboratory results for a humidity range between 30% and 90% for both pulse wavelengths: including step responses, humidity cycles and hysteresis analysis. In addition to the attenuation and backscatter coefficient dependence, we also analyze optical runtime changes as a function of humidity. POFs have the advantage that they can be directly embed-ded into materials such as concrete or soil to measure water content or localize water ingress. Standard step-index PMMA POFs can be used as a distributed relative humidity sensor up to 200 m distance.
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 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.
Fibre-reinforced plastics (FRP) especially carbon-fibre-reinforced polymer (CFRP) and glass-fibre-reinforced polymer (GFRP) are commonly used materials in high pressure vessels and storage units for automotive and aerospace purposes. Optical fibres are suitable to be integrated or directly applied to the surface of FRP components. Using optical fibres it is possible to monitor the distributed strain profiles and changes within the fatigue life of a pressure vessel to ensure the operational safety. Within artificial ageing experiments we used swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of commercial high-pressure composite cylinder. This artificial ageing was performed using test conditions of 503bar pressure load (service pressure 300 bar) and 89 °C for 100 h. The polyimide coated optical fibres were glued to the surface externally in circumferential and axial direction. Using distributed strain sensing (DSS) material expansion of over 0.5% were monitored with sub-centimetre spatial resolution. Within the circumferential direction we observed up to 10 % local fluctuation compared to the median strain caused by inhomogeneous material expansion, which could cause local material fatigue. In addition, we determined material degradation manifested itself as localized remaining material expansion and/or contraction. Results have been validated by other non-destructive methods like digital strip projection.
Fibre-reinforced plastics (FRP) especially carbon-fibre-reinforced polymer (CFRP) and glass-fibre-reinforced polymer (GFRP) are commonly used materials in high pressure vessels and storage units for automotive and aerospace purposes. Optical fibres are suitable to be integrated or directly applied to the surface of FRP components. Using optical fibres it is possible to monitor the distributed strain profiles and changes within the fatigue life of a pressure vessel to ensure the operational safety. Within artificial ageing experiments we used swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of commercial high-pressure composite cylinder. This artificial ageing was performed using test conditions of 503bar pressure load (service pressure 300 bar) and 89 °C for 100 h. The polyimide coated optical fibres were glued to the surface externally in circumferential and axial direction. Using distributed strain sensing (DSS) material expansion of over 0.5% were monitored with sub-centimetre spatial resolution. Within the circumferential direction we observed up to 10 % local fluctuation compared to the median strain caused by inhomogeneous material expansion, which could cause local material fatigue. In addition, we determined material degradation manifested itself as localized remaining material expansion and/or contraction. Results have been validated by other non-destructive methods like digital strip projection.
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.
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.
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.
The application of composite pressure vessels is of great interest due to tremendous weight saving. Nevertheless, ageing of CFRP (carbon fiber reinforced plastic) composites pressure vessels underlies complex interactions between metallic liner and composite and is not fully understood yet. One main challenge is the application of appropriate testing methods for detecting the influences of pressure cycles and creep behavior on the material. This contribution presents results achieved by using conventional eddy current testing (ET) and high frequency ET. Here, we could show that also conventional ET with low frequencies are useable to investigate pressure vessels of different material combinations like aluminum-CFRP and synthetics-CFRP.
We present results demonstrating several beneficial effects on distributed fiber optic vibration sensing (DVS) functionality and performance resulting from utilizing standard single mode optical fiber (SMF) with femtosecond laser-inscribed equally-spaced simple scattering dots. This modification is particularly useful when using traditional single-wavelength amplitude-based coherent optical time domain reflectometry (C-OTDR) as sensing method. Local sensitivity is increased in quasi-distributed interferometric sensing zones which are formed by the fiber segments between subsequent pairs of the scattering dots. The otherwise nonlinear transfer function is overwritten with that of an ordinary two-beam interferometer. This linearizes the phase response to monotonous temperature variations. Furthermore, sensitivity fading is mitigated and the demodulation of low-frequency signals is enabled. The modification also allows for the quantitative determination of local temperature gradients directly from the C-OTDR intensity traces. The dots’ reflectivities and thus the induced attenuation can be tuned via the inscription process parameters. Our approach is a simple, robust and cost-effective way to gain these sensing improvements without the need for more sophisticated interrogator technology or more complex fiber structuring, e.g., based on ultra-weak FBG arrays. Our claims are substantiated by experimental evidence.
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.
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.
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.
The feasibility study „AGIFAMOR. Ageing infrastructures – distributed acoustic monitoring of pipes” is an interdisciplinary research project at BAM internally financed from 2015 to 2018. Therefore, the quite young fibre optic sensing technology of distributed acoustic sensing (DAS) was investigated to possibly be extended towards a global condition monitoring system for pipelines operating in real time.
DAS is a highly dynamic fibre optic sensing technology based on the method of coherent optical time domain reflectometry (C-OTDR). DAS allows capturing strain changes in the range of kHz. For the experimental work, the most suitable application yielding an optimum sensitivity was proven by wrapping a standard single-mode silica fibre around the pipe.
The DAS sensitivity was investigated regarding the detection of 1) incidents that initiate propagation of acoustic waves in the pipe wall, 2) changes inside the pipeline causing altered flow and 3) damage development in the pipe wall. Therefore, several testing setups in laboratory as well as in real scale were realized. For comparison purposes, experiments were accompanied by acoustic emission analyses and by measurements with accelerometers.
DAS was found to be very sensitive to gas ignition and its propagation across the pipe. Furthermore, the ability of DAS to detect and localize acoustic signals associated with pipeline leakage was demonstrated. The detection of crack formation and propagation within the pipe wall by means of DAS was studied during bending tests on several pipe segments, but was not proven so far with certainty. As expected, these studies turned out as the most difficult challenge due to the random occurrence and transient nature of microscopic damage phenomena.
Feasibility study: Continuous monitoring of pipes using distributed acoustic and fibre optic sensors
(2018)
The feasibility study „AGIFAMOR. Ageing infrastructures – distributed acoustic monitoring of pipes” is an interdisciplinary research project at BAM internally financed from 2015 to 2018. Therefore, the quite young fibre optic sensing technology of distributed acoustic sensing (DAS) was investigated to possibly be extended towards a global condition monitoring system for pipelines operating in real time.
DAS is a highly dynamic fibre optic sensing technology based on the method of coherent optical time domain reflectometry (C-OTDR). DAS allows capturing strain changes in the range of kHz. For the experimental work, the most suitable application yielding an optimum sensitivity was proven by wrapping a standard single-mode silica fibre around the pipe.
The DAS sensitivity was investigated regarding the detection of 1) incidents that initiate propagation of acoustic waves in the pipe wall, 2) changes inside the pipeline causing altered flow and 3) damage development in the pipe wall. Therefore, several testing setups in laboratory as well as in real scale were realized. For comparison purposes, experiments were accompanied by acoustic emission analyses and by measurements with accelerometers.
DAS was found to be very sensitive to gas ignition and its propagation across the pipe. Furthermore, the ability of DAS to detect and localize acoustic signals associated with pipeline leakage was demonstrated. The detection of crack formation and propagation within the pipe wall by means of DAS was studied during bending tests on several pipe segments, but was not proven so far with certainty. As expected, these studies turned out as the most difficult challenge due to the random occurrence and transient nature of microscopic damage phenomena.
The in-situ monitoring of electrochemical deposition (ECD) processes is still a challenge regarding the measurement of the effective temperature of the substrate and the formation of mechanical stress in the layer under given plating conditions. Monitoring problems can be solved by applying a pre-coated fiber Bragg grating (FBG) to the electrolytic process as the shift of the Bragg wavelength is affected by both the temperature of the electrolyte near the substrate and the stress formation in the growing layer. The experimental FBG set-up and the quantitative determination of temperature- and stress-related strain is described for a nickel-iron electrolyte.
Fiber optic Distributed Acoustic Sensing (DAS) is an emerging method for many different monitoring purposes, enabling a spatially and temporally resolved collection of acoustic and vibration information over many kilometers. DAS, thus being a "dynamic" sensing technique, allows for online condition monitoring and the detection and localization of threats or hazards in real time via characteristic acoustic/vibration states and their changes or via occurring anomalous signals, respectively.
At BAM, we have employed this technology for a number of different applications of monitoring of large infrastructures, e.g., bridges, pipelines, submarine power cables or railway tracks. Currently, we are investigating the use of DAS for further innovative uses, which aim at facilitating the energy transition, enabling "smart" infrastructure and providing the basis for comprehensive hazard monitoring and warning systems, respectively. Our research fields include fiber optic borehole monitoring in the context of hydrogen storage caverns, traffic and road monitoring, using DAS for earthquake monitoring in urban areas for disaster management and long-term monitoring of large-scale subsidence caused by mining activities. Another new topic is structural health monitoring in concrete structures in the framework of the FSP Security.
In this talk, we will first briefly present the basics and capabilities of DAS. Then, we will portray our previous and current works related to this technology and show some attractive results. Finally, we will discuss our upcoming projects on exciting new applications of DAS.
For the purpose of increasing payload and reduce freight cost, lightweight composite tank containers used for Transportation have been progressively developed during the last years. Compared to conventionally produced cylindrical steel tanks, the fiber-reinforced solutions allow greater flexibility in the tank design. Despite a number of further material-related benefits of fiber-reinforced composites as non-conductive and non-magnetic behavior as well as corrosion resistance and high strength, the optimization of their thermal degradation properties during combustion is still a challenge. To improve the fire performance of lightweight composite containers, special intumescent fire protection coatings can be applied onto the outside tank surface. This paper presents fire tests on glass-fiber-reinforced plastic transport tanks with complex geometries sheltered with different surface-applied fire protection systems. To evaluate the fire resistance of the tank structures, a fiber optic monitoring system was developed. This system is based on distributed temperature measurements using high-Resolution optical backscatter reflectometry and pointwise reference measurements using fiber Bragg gratings. Thereby, all the fiber optic sensors were directly integrated in the composite layer structure of the tanks. The focus of the presented work is on the demonstration of capability of fiber optic monitoring system in such high-temperature application. Moreover, the fiber optic measurements provide new insights into the efficiency of intumescent coating applied for fire protection of fiber-reinforced plastic transport tanks.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.
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.
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.
The lack of on-line validation procedures for structure-embedded fiber-optical strain sensors, in particular fiber-Bragg-gratings (FBG), resulted in limited applications in structural health monitoring (SHM). Degradation under service conditions and ageing as a result of climatic influences or delamination under load were unsolved Validation issues. This could be overcome by means of an auto-diagnosis procedure based on FBG-sensors coated by electrochemical deposition (ECD) with a magnetostrictive NiFe-coating on top of an adhesive Cu/Cr adhesive layer deposited by physical vapour deposition (PVD) around the FBG strain sensor. This allows at any time under Service a validation of sensor functionality, stability, and reliability. For this purpose, a magnetic strain-proportional reference field is introduced. The optical read-out is realized by the measurement of the Bragg-wavelength shift.
The ratio of resulting strain and exciting magnetic reference field should be constant given that the sensor is in proper function.
In principle, the magnetostrictive coating around the FBG should also work as on-line magnetic field sensor and other applications in material science. One of these applications is the in-situ monitoring of ECD processes as the deposition of the ECD NiFe-layer on the FBG revealed. Challenges are the monitoring of temperature, deposition
stages/thickness, and resulting mechanical stress under given plating conditions. Monitoring problems can be solved by applying a pre-coated FBG to the electrolytic process as the shift of the Bragg wavelength is affected by both the temperature of the electrolyte near the substrate and the stress formation in the growing layer. The experimental FBG set-up and the quantitative determination of temperature- and stress-related strain are described for a nickeliron
electrolyte. The in-situ measurement of Bragg wavelength shifts of a pre-coated FBG during electrochemical deposition allows a detailed analysis of stress states due to changes in the growth morphology of the layer. The separation of mechanical and thermal contributions to this shift provides information on the individual Deposition processes in terms of a process fingerprint.
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.
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.
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.
We report, to our knowledge, for the first time on humidity-induced Brillouin frequency shifts in perfluorinated graded index polymer optical fibers. A linear relation between Brillouin frequency shift and humidity was observed. Furthermore, the humidity coefficient of the Brillouin frequency shift is demonstrated to be a function of temperature (-107 to -64 kHz/%r.h. or -426 to -49 kHz m3/g in the range of 20 to 60 °C). An analytical description proves temperature and humidity as two mutually independent effects on the Brillouin frequency shift.
The competitiveness of the process industry is based on ensuring the required product quality while making optimum use of equipment, raw materials and energy. Chemical 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. The potential of digital technologies belongs to these.
One way is knowledge-based production, taking into account all essential equipment, process and regulatory data of plants and laboratories. Today, the potential of this data is often not yet consistently used for a comprehensive understanding of production. Another approach uses flexible and modular chemical plants, 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. Digital transformation is enabling completely new production concepts that are being used increasingly. Intensified continuous production plants also allow for difficult to produce compounds.
This contribution aims to encourage a more holistic approach to the digitalization and use of machine-assisted methods in (bio) process engineering by introduction of integrated and networked systems and processes, which have the potential to speed up the high-quality production of specialty chemicals and pharmaceuticals.
Introduction to group 8.6
(2023)
Humidity is one of principal environmental parameters that plays an important role in various application areas. Distributed humidity/water sensing is sought after in wide range of applications including concrete condition monitoring in civil engineering, SHM of large structures such as dykes or dams, soil moisture measurement in agriculture or leak detection in pipeline or sewage industry. Using humidity-induced strain of specialty hygroscopic coating materials, such as polyimide (PI) seems as the most promising approach so far. In this work, relative humidity and temperature response of different commercial PI-coated and tight-buffer fibers is investigated for the development of distributed humidity sensor.
Humidity is one of principal environmental parameters that plays an important role in various application areas. Distributed humidity/water sensing is sought after in wide range of applications including concrete condition monitoring in civil engineering, SHM of large structures such as dykes or dams, soil moisture measurement in agriculture or leak detection in pipeline or sewage industry. Using humidity-induced strain of specialty hygroscopic coating materials, such as polyimide (PI) seems as the most promising approach so far. In this work, relative humidity and temperature response of different commercial PI-coated and tight-buffer fibers is investigated for the development of distributed humidity sensor.
Humidity is one of principal environmental parameters that plays an important role in various application areas. Using measurement of strain induced in an optical fiber by a water swellable coating represents a promising approach for realization of distributed humidity sensing (DHS). In this work, humidity and temperature response of four different commercial PI-coated fibers and four tight-buffered (TB) fibers is investigated with the aim of evaluating their potential for development of DHS in context of water ingress sensor for high-voltage power cable splices. PI-coated fibers exhibited close-to-linear humidity and temperature response. While the temperature response is relatively coating-independent, magnitude of humidity response was broadly correlated to the relative fiber-to-coating thickness ratio. In contrast, both humidity and temperature response of TB fibers is strongly influenced by buffer type, with Leoni TB900L fiber with Hytrel buffer exhibiting largest humidity and temperature sensitivity. While the response of tight-buffered fibers is generally nonlinear, roughly three-times higher humidity response can be achieved with TB900L compared to the most sensitive PI-coated fiber. Using the TB fiber can be, therefore, advantageous for simpler water detection applications, such as one targeted in this study, when larger sensitivity is more important than the linear response of the sensor.
In this paper perfluorinated graded-index polymer optical fibers are characterized with respect to the influence of relative humidity changes on spectral transmission absorption and Rayleigh backscattering. The hygroscopic and thermal expansion coefficient of the fiber are determined to be CHE = (7.4 +/- 0.1) 10^-6 %r.h.^-1 and CTE = (22.7 +/- 0.3) 10^-6 K^-1, respectively. The influence of humidity on the Brillouin backscattering power and linewidth are presented for the first time to our knowledge. The Brillouin backscattering power at a pump wavelength of 1319 nm is affected by temperature and humidity. The Brillouin linewidth is observed to be a function of temperature but not of humidity. The strain coefficient of the BFS is determined to be CS = (146.5 +/- 0.9) MHz/% for a wavelength of 1319 nm within a strain range from 0.1 % to 1.5 %. The obtained results demonstrate that the humidity-induced Brillouin frequency shift is predominantly caused by the swelling of the fiber over-cladding that leads to fiber straining.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication:
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst.
For an online live demonstration using a pre-trained network have a look at github.
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.
Pipe integrity is a central concern regarding technical safety, availability, and environmental compliance of industrial plants and pipelines. A condition monitoring system that detects and localizes threats in pipes prior to occurrence of actual structural failure, e.g., leakages, especially needs to target transient events such as impacts on the pipe wall or pressure waves travelling through the medium. In the present work, it is shown that fiber-optic distributed acoustic sensing (DAS) in conjunction with a suitable application geometry of the optical fiber sensor allows to track propagating acoustic waves in the pipeline wall on a fast time-scale. Therefore, short impacts on the pipe may be localized with high fidelity. Moreover, different acoustic modes are identified, and their respective group velocities are in good agreement with theoretical predications. In another set of experiments modeling realistic damage scenarios, we demonstrate that pressure waves following explosions of different gas mixtures in pipes can be observed. Velocities are verified by local piezoelectric pressure transducers. Due to the fully distributed nature of the fiber-optic sensing system, it is possible to record accelerated motions in detail. Therefore, in addition to detection and localization of threatening events for infrastructure monitoring, DAS may provide a powerful tool to study the development of gas explosions in pipes, e.g., investigation of deflagration-to-detonation-transitions (DDT).
A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification.
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 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.
Short presentation of the PhD project in machine learning based Brillouin distributed sensing. Machine learning can be used to enhance the performance of BOFDA and reduce considerably the measurement time. Apart from this, ML can also be used to extract more information from the Brillouin gain spectrum in order to render the temperature and strain discrimination possible
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.