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Organisationseinheit der BAM
- 8.4 Akustische und elektromagnetische Verfahren (69) (entfernen)
Hydrogen is an energy source of increasing importance. As hydrogen is very reactive to air and needs to be stored under high pressure, it is crucial to provide safe transportation and storage. Therefore, structural health monitoring, based on guided ultrasonic waves and machine learning methods, is used for Composite Overwrapped Pressure Vessels (COPVs) containing hydrogen. To acquire data that allows robust detection of COPV defects, there are two main process parameters to consider. These are the pressurization of the vessel and the temperature conditions at the vessel. This paper will focus on the derivation of a design of experiment (DoE) from the needs of various validation scenarios (e.g. concerning pressure, temperature or excitation frequency). Practical limitations must be considered as well. We designed experiments with multiple reversible damages at different positions. A network of 25 transducers, structured as five rings with five sensors in one line, is installed on a vessel. Guided ultrasonic waves are used via the pitch-catch procedure, which means that the transducers act pairwise as transmitter and receiver in order to measure all transmitterreceiver combinations. This leads to 600 signal paths, recorded by a Verasonics Vantage 64 LF data acquisition system. Finally, the influences of temperature and pressure within the acquired data set are going to be visualized.
Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.
This article presents a method to use the dispersive behavior of ultrasonic guided waves and neural networks to determine the isotropic elastic constants of plate-like structures through dispersion images. Therefore, two different architectures are compared: one using convolutions and transfer learning based on the EfficientNetB7 and a Vision Transformer-like approach. To accomplish this, simulated and measured dispersion images are generated, where the first is applied to design, train, and validate and the second to test the neural networks. During the training of the neural networks, distinct data augmentation layers are employed to introduce artifacts appearing in measurement data into the simulated data. The neural networks can extrapolate from simulated to measured data using these layers. The trained neural networks are assessed using dispersion images from seven known material samples. Multiple variations of the measured dispersion images are tested to guarantee the prediction stability. The study demonstrates that neural networks can learn to predict the isotropic elastic constants from measured dispersion images using only simulated dispersion images for training and validation without needing an initial guess or manual feature extraction, independent of the measurement setup. Furthermore, the suitability of the different architectures for generating information from dispersion images in general is discussed.
Flexible anisotropic magnetoresistive sensors for novel magnetic flux leakage testing capabilities
(2024)
Rigid magnetic field sensors such as anisot Hall sensors have been used for years and have become industry standard for electromagnetic non-destructive testing (NDT). Recent technological developments in the field of flexible electronics allow for the fabrication of reshapeable magnetic field sensors on flexible substrates via thin-film deposition or printing. The magnetic properties of these sensors have comparable characteristics to industry-standard rigid magnetic field sensors, with the added ability of adapting to the surface of complex components and scanning in contact with the sample surface. This improves defect detectability and magnetic signal strength by minimizing the scanning lift-off (LO) distance. In this article flexible AMR sensors mounted on a rotative mechanical holder were used to scan a semi-circular ferromagnetic sample with 3 reference defects via magnetic flux leakage (MFL) testing, thus demonstrating the applicability of this type of sensors for the scanning of curved samples. In order to benchmark the performance of these sensors in comparison to industry standard rigid magnetic field sensors, a ferromagnetic sample with 10 reference defects of different depths was scanned employing flexible AMR and rigid GMR sensors. Defects with depths ranging from 110 μm up to 2240 μm were detected with an signal-tonoise ratio (SNR) of 2.7 up to 27.9 (for flexible AMR sensors) and 6.2 up to 72.3 (for rigid GMR sensors), respectively. A 2D magnetometer mapping of the sample with a spatial scanning step of 10 × 50 μm2 (flexible AMR) and 16 × 100 μm2 (rigid GMR) was obtained. The results show that this type of sensor can be used for high-resolution and high-detail mapping of defects on the surface of planar and non-planar ferromagnetic samples since the scanning lift-off distance is equal to the substrate thickness of 20 μm for in-contact scanning. The SNR comparison between flexible and rigid sensors shows that the performance of the flexible AMR sensors employed is not very far behind the performance of the rigid GMR sensors used.
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.
Normung und Standardisierung in der zerstörungsfreien Prüfung finden auf nationaler, europäischer und internationaler Ebene statt. Die Übernahme von Leitungsfunktionen in diesen Gremien erzeugt die Möglichkeit der Steuerung von Themen und Inhalten der relevanten Normen. Das persönliche Engagement der Fachexpert*innen in den nationalen Gremien und den europäischen und internationalen Arbeitsgruppen ist ein zusätzlicher Faktor für die inhaltliche Korrektheit und die inhaltliche Ausrichtung der Norm. Denn wer nicht normt, wird genormt und muss letztlich Normen anwenden, die andere entsprechend ihrer eigenen Interessen gestaltet haben. Voraussetzung für die Teilnahme in europäischen und internationalen Normungsgremien ist die Mitwirkung in den entsprechenden DINSpiegelgremien, welche die europäischen (CEN) und internationalen (ISO) Aktivitäten spiegeln und die Fachexpert*innen für die Mitarbeit autorisieren.
Auf internationaler Ebene findet die Normungsarbeit in den technischen Komitees ISO/TC 135 “Non-destructive testing” und ISO/TC 44/SC 5 “Testing and inspection of welds” und ihren Arbeitsgruppen statt. Das europäische Pendant ist das CEN/TC 138 “Zerstörungsfreie Prüfverfahren” mit seinen Arbeitsgremien. Die DINSpiegelgremien sind für die deutsche Mitarbeit in diesen Gremien und die inhaltliche Bewertung der Norm-Entwürfe zuständig.
Bei der zerstörungsfreien Prüfung verlegter Eisenbahnschienen werden die Rohdaten derzeit in proprietären Datenformaten gespeichert und auf Datenträgern zwischen den Prüfzügen und den auswertenden Stellen versendet. Die proprietären Datenformate sind in der Regel nur den Herstellern der Prüfsysteme bekannt und deren Dokumentation nicht allgemein zugänglich.
Die „Standard Practice for Digital Imaging and Communication in Nondestructive Evaluation“ (DICONDE), basierend auf dem medizinischen Standard „Digital Imaging and Communication in Medicine“ (DICOM), ermöglicht es, sowohl Prüfdaten als auch Prüfergebnisse und Streckeninformationen in einem standardisierten Format zu speichern und zwischen verschiedenen Endpunkten zu übertragen.
Das Poster gibt zunächst einen kurzen Überblick über die hierarchische Struktur von DICONDE und zeigt dann, wie DICONDE bei der Prüfung verlegter Eisenbahnschienen verwendet werden kann. Die geometrischen Besonderheiten (mehrere Kilometer Länge pro Prüffahrt, kurviger Streckenverlauf) stellen dabei eine besondere Herausforderung dar. Im Rahmen des mFUND-geförderten Projektes „Arteficial Intelligence for Railway Inspection (AIFRI)“, Förderkennzeichen 19FS2014C, wurde ein Vorschlag für eine Erweiterung des DICONDE-Standards für die Schienenprüfung erarbeitet und bei der ASTM eingereicht.
Early detection of fatigue cracks and accurate measurements of the crack growth play an important role in the maintenance and repair strategies of steel structures exposed to cyclic loads during their service life. Observation of welded connections is especially of high relevance due to their higher susceptibility to fatigue damage. The aim of this contribution was to monitor fatigue crack growth in thick welded specimens during fatigue tests as holistically as possible, by implementing multiple NDT methods simultaneously in order to record the crack initiation and propagation until the final fracture. In addition to well-known methods such as strain gauges, thermography, and ultrasound, the crack luminescence method developed at the Bundesanstalt für Materialforschung und -prüfung (BAM), which makes cracks on the surface particularly visible, was also used. For data acquisition, a first data fusion concept was developed and applied in order to synchronize the data of the different methods and to evaluate them to a large extent automatically. The resulting database can ultimately also be used to access, view, and analyze the experimental data for various NDT methods. During the conducted fatigue tests, the simultaneous measurements of the same cracking process enabled a comprehensive comparison of the methods, highlighting their individual strengths and limitations. More importantly, they showed how a synergetic combination of different NDT methods can be beneficial for implementation in large-scale fatigue testing but also in monitoring and inspection programs of in-service structures - such as the support structures of offshore wind turbines.
Offshore wind turbines continuously increase in size and weight and demand adequate offshore foundations concepts like monopiles, tripods, or jackets. These components are typically constructed using submerged arc welding (SAW) with high-strength thick steel plates like the S420ML. During welding, the occurrence of delayed hydrogen-assisted cracking (HAC) must be anticipated. HAC is a critical combination of the local hydrogen concentration within a susceptible microstructure under certain mechanical load, i.e., the occurring (welding) residual stresses. The welding sequence of the thick-walled plates complicates the residual stress distribution due to the necessary repeated thermal cycling, i.e., welding seam/layer deposition to fill the joint. For that purpose, SAW with two-wire-technique was used to weld a specially designed and prototype-like mock-up of a real component with a thickness of 50 mm, filled with over 20 passes and a seam length of 1000 mm. Additional welded stiffeners simulated the effect of a high restraint, to achieve critical HAC conditions. The necessity of a minimum waiting time (MWT) before the NDT can be conducted (to exclude HAC) was critically verified by the application of ultrasonic testing of the welded joint at different time-steps of the NDT of up to 48 h after the completion welding. The residual stresses were determined by a robot XRD goniometer. Tensile residual stresses up to the yield limit are found both in the weld metal and in the heat-affected zone. Numerical modeling allowed the qualitative estimation of the hydrogen diffusion in the weld. No noticeable HAC occurrence was identified and confirms the high cracking resistance of the investigated material. Finally, the applicability of the MWT concept should be critically discussed.