8.6 Faseroptische Sensorik
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- 2021 (14) (entfernen)
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Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (14)
- 8.6 Faseroptische Sensorik (14)
- 1 Analytische Chemie; Referenzmaterialien (4)
- 1.4 Prozessanalytik (4)
- 8.1 Sensorik, mess- und prüftechnische Verfahren (3)
- 3 Gefahrgutumschließungen; Energiespeicher (2)
- 3.5 Sicherheit von Gasspeichern (2)
- 8.4 Akustische und elektromagnetische Verfahren (2)
- 5 Werkstofftechnik (1)
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Eingeladener Vortrag
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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.
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.
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.
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 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.
Brillouin Optical frequency domain analysis (BOFDA) is a well-known technique in distributed Fiber sensing for temperature and strain monitoring. In this study, we applied a signal post-processing method based on convolutional neural networks (CNNs) and we show that in comparison to conventional methods based on Lorentzian curve fitting (LCF), our CNN model is more robust against noise and can shorten the measurement time by more than 9 times. This will open the way for applications, where faster monitoring is needed.
The feasibility of traffic monitoring along a major urban road using Distributed Acoustic Sensing (DAS) is demonstrated. We present measurement results of conventional intensity-based DAS along a dark fiber in a tubed fiber optic cable buried roadside. With only minimal postprocessing different classes of traffic participants can be distinguished (cars, bikes, pedestrians). Different methods for pre-processing raw data are compared, especially with regard to providing suitable inputs for pattern recognition algorithms. Furthermore, the various challenges for automatized vehicle detection and classification related to varying sensitivity and inhomogeneous signal propagation are discussed. Sensitivity fluctuations and variations are in part inherent to the measurement technology and originate in part in ground conditions. The measurement quality and usefulness for traffic monitoring of intensity-based DAS is compared to that of state-of-the-art phase-resolved DAS, allowing quantitative evaluation of vibration signals.
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.