TY - JOUR A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Mesnil, O. A1 - Prager, Jens A1 - Gohlke, Dirk A1 - Boller, C. T1 - Damage quantification in an aluminium-CFRP composite structure using guided wave wavenumber mapping: Comparison of instantaneous and local wavenumber analyses N2 - Composite-overwrapped pressure vessels (COPV) are increasingly used in the transportation industry due to their high strength to mass ratio. Throughout the years, various designs were developed and found their applications. Currently, there are five designs, which can be subdivided into two main categories - with a load-sharing metal liner and with a non-load-sharing plastic liner. The main damage mechanism defining the lifetime of the first type is fatigue of the metal liner, whereas for the second type it is fatigue of the composite overwrap. Nevertheless, one damage type which may drastically reduce the lifetime of COPV is impact-induced damage. Therefore, this barely visible damage needs to be assessed in a non-destructive way to decide whether the pressure vessel can be further used or has to be put out of service. One of the possible methods is based on ultrasonic waves. In this contribution, both conventional ultrasonic testing (UT) by high-frequency bulk waves and wavenumber mapping by low frequency guided waves are used to evaluate impact damage. Wavenumber mapping techniques are first benchmarked on a simulated aluminium panel then applied to experimental measurements acquired on a delaminated aluminium-CFRP composite plate which corresponds to a structure of COPV with a load-sharing metal liner. The analysis of experimental data obtained from measurements of guided waves propagating in an aluminium-CFRP composite plate with impact-induced damage is performed. All approaches show similar performance in terms of quantification of damage size and depths while being applied to numerical data. The approaches used on the experimental data deliver an accurate estimate of the in-plane size of the large delamination at the aluminium-CFRP interface but only a rough estimate of its depth. Moreover, none of the wavenumber mapping techniques used in the study can quantify every delamination between CFRP plies caused by the impact, which is the case for conventional UT. This may be solved by using higher frequencies (shorter wavelengths) or more advanced signal processing techniques. All in all, it can be concluded that imaging of complex impact damage in fibre-reinforced composites based on wavenumber mapping is not straightforward and stays a challenging task. KW - Ultrasonic guided waves KW - Instantaneous wavenumber analysis KW - Local wavenumber analysis KW - Signal processing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-528203 DO - https://doi.org/10.1016/j.ndteint.2021.102472 SN - 0963-8695 VL - 122 SP - 2472 PB - Elsevier Ltd. CY - Amsterdam, Netherlands AN - OPUS4-52820 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hassenstein, Christian A1 - Heckel, Thomas A1 - Prager, Jens A1 - Berendt, T. A1 - Voehringer, D. T1 - Robotergeführte Ultraschallprüfung von Gasturbinenschaufeln mit adaptiver TFM im ZfP 4.0-Umfeld N2 - Leit- und Laufschaufeln stationärer Gasturbinen sind im Betrieb anspruchsvollen Bedingungen ausgesetzt. Die Prüfung, Wartung und Reparatur der hochbelasteten Teile sind notwendig, um Sicherheit und Effizienz über längere Zeiträume zu gewährleisten. Eine der in diesem Prozess angewandten Methoden ist die Ultraschallprüfung. Im Zuge der fortschreitenden Digitalisierung ist es das Ziel des Projektes MRO 2.0, den Aufbereitungsvorgang um digitale Prozesse zu erweitern und somit zu modernisieren. Eine der Aufgaben ist es, dabei anstelle der konventionellen Ultraschallprüfung eine Array-Technik mit rekonstruierenden Verfahren einzusetzen und eine automatisierte, digital eingebundene Methode im Rahmen von ZfP 4.0 zu entwickeln. Um die Ist-Geometrie der Oberfläche und die verbleibende Wanddicke flächendeckend an den Turbinenschaufeln zu bestimmen, wird ein adaptives TFM Verfahren (Total Focusing Method) in Tauchtechnik eingesetzt, welches die Brechung der Schallwellen an der Grenzfläche zum Prüfkörper in der Rekonstruktion berücksichtigt. In der hierfür entwickelten Prüfanlage führt ein Roboterarm einen Array-Prüfkopf und scannt das Bauteil normal zur inspizierten Oberfläche. Somit kann ein 3D-Modell des Ist Zustandes des zu prüfenden Bauteils erstellt werden. Spezielle Herausforderungen an die Aufgabe stellen die komplexe Geometrie des Prüfobjektes sowie dessen geringe und ortsabhängige Wanddicke dar. Ein weiterer Fokus liegt auf der Optimierung der Prüfparameter, Verbesserung der Prüfgeschwindigkeit sowie der Zuverlässigkeit der Methode. In diesem Beitrag werden die während des Projektes entwickelte Methode und die im zweiten Projektjahr erarbeiteten Ergebnisse vorgestellt, mit dem Schwerpunkt auf die entwickelten Rekonstruktionsalgorithmen und den Versuchsaufbau. T2 - DGZfP Jahrestagung 2022 CY - Kassel, Germany DA - 23.05.2022 KW - Ultraschallprüfung KW - Turbinenschaufel KW - Phased Array KW - Total Focusing Method KW - Automatisierung KW - Tauchtechnik PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-551881 UR - https://www.ndt.net/search/docs.php3?id=27055 SP - 1 EP - 11 PB - NDT.net AN - OPUS4-55188 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schnur, C. A1 - Goodarzi, P. A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Prager, Jens A1 - Tschöke, K. A1 - Moll, J. A1 - Schütze, A. A1 - Schneider, T. T1 - Towards interpretable machine learning for automated damage detection based on ultrasonic guided waves N2 - Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. KW - Composite structures KW - Structural health monitoring KW - Carbon fibre-reinforced plastic KW - Interpretable machine learning KW - Automotive industry PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542060 DO - https://doi.org/10.3390/s22010406 SN - 1424-8220 VL - 22 IS - 1 SP - 1 EP - 19 PB - MDPI CY - Basel AN - OPUS4-54206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Charmi, Amir A1 - Mustapha, Samir A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Prager, Jens T1 - A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels N2 - The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy. T2 - 13th European Conference on Non-Destructive Testing (ECNDT) 2023 CY - Lisbon, Portugal DA - 03.07.2023 KW - Machine learning KW - Structural health monitoring KW - COPV KW - Guided waves KW - Damage localization PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-590789 DO - https://doi.org/10.58286/28079 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59078 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mustapha, S. A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Brence, Blaz A1 - Prager, Jens T1 - Guided Waves Propagation in Composite Overwrapped Pressure Vessel N2 - The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification. T2 - 13th. European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Composite Overwrapped Pressure Vessels KW - Guided Waves KW - Laser Doppler Vibrometer KW - Structural Health Monitoring KW - Sensors Placement PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-591081 DO - https://doi.org/10.58286/28068 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Mustapha, S. A1 - Charmi, Amir A1 - Prager, Jens T1 - Guided wave ultrasonic feature determination in Type IV composite overwrapped pressure vessels towards the digital twin N2 - The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations. T2 - 13th European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Industry 4.0 KW - Composite overwrapped pressure vessels KW - Structural health monitoring KW - Ultrasonic guided waves KW - Predictive maintenance PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-591094 DO - https://doi.org/10.58286/28072 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59109 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bulling, Jannis A1 - Jurgelucks, B. A1 - Prager, Jens A1 - Walther, A. T1 - Defect reconstruction in a two-dimensional semi-analytical waveguide model via derivative-based optimization N2 - This paper considers an indirect measurement approach to reconstruct a defect in a two-dimensional waveguide model for a non-destructive ultrasonic inspection via derivative-based optimization. The propagation of the mechanical waves is simulated by the scaled boundary finite element method that builds on a semi-analytical approach. The simulated data are then fitted to given data associated with the reflected waves from a defect which is to be reconstructed. For this purpose, we apply an iteratively regularized Gauss-Newton method in combination with algorithmic differentiation to provide the required derivative information accurately and efficiently. We present numerical results for three kinds of defects, namely, a crack, delamination, and corrosion. The objective function and the properties of the reconstruction method are investigated. The examples show that the parameterization of the defect can be reconstructed efficiently as well as robustly in the presence of noise. KW - Mechanical waves KW - Corrosion KW - Finite-element analysis KW - Ultrasonic testing KW - Nondestructive testing techniques KW - Symbolic computation KW - Materials analysis KW - MATLAB KW - Newton Raphson method PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-565485 DO - https://doi.org/10.1121/10.0013574 VL - 152 IS - 2 SP - 1217 EP - 1229 PB - AIP Publ. CY - Melville, NY AN - OPUS4-56548 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - El Moutaouakil, H. A1 - Fuchs, C. A1 - Savli, E. A1 - Heimann, Jan A1 - Prager, Jens A1 - Moll, J. A1 - Tschöke, K. A1 - Márquez Reyes, O. A1 - Schackmann, O. A1 - Memmolo, V. A1 - Schneider, T. T1 - Acquiring a Machine Learning Data Set for Structural Health Monitoring of Hydrogen Pressure Vessels at Operating Conditions using Guided Ultrasonic Waves N2 - 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. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Composite Overwrapped Pressure Vessel KW - Hydrogen KW - Guided Ultrasonic Waves KW - Data Acquisition KW - Pressurization KW - Machine Learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607472 DO - https://doi.org/10.58286/29754 SN - 1435-4934 SP - 1 EP - 8 AN - OPUS4-60747 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir A1 - Heimann, Jan A1 - Duffner, Eric A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading N2 - 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. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Structural health monitoring KW - Deep learning KW - Ultrasonic-guided waves KW - Anomaly detection KW - Damage localization PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607396 DO - https://doi.org/10.58286/29751 SP - 1 EP - 8 AN - OPUS4-60739 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schackmann, Oliver A1 - Márquez Reyes, Octavio A. A1 - Memmolo, Vittorio A1 - Lozano, Daniel A1 - Prager, Jens A1 - Moll, Jochen A1 - Kraemer, Peter T1 - Machine learning strategies with ensemble voting for ultrasonic damage detection in composite structures under varying temperature or load conditions N2 - In recent years, the development of machine learning (ML) techniques has led to significant progress in the field of structural health monitoring with ultrasonic-guided waves. However, a number of challenges still need to be resolved for reliable operation in realistic settings. In this work, we consider the complex problem of experimental damage detection under varying temperature or load conditions where damage locations are not included in the training set. The ML techniques proposed here include supervised and unsupervised methods originally developed for image and time series classification combined with ensemble voting. A performance demonstration of the ML techniques is presented using benchmark datasets from the open-guided waves platform. The unsupervised approach is then applied to a new dataset from an experimental campaign carried out on a composite over-wrapped pressure vessel used for hydrogen storage with real defects. Results show that ensemble voting enables the effective combination of the predictions of multiple transducer pairs, even with a limited number of strong individual classifiers. When applied to unsupervised learning, this returns high accuracy also when real damage over the structure is considered. KW - Structural-Health-Monitoring KW - Ultrasonic guided waves PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631571 DO - https://doi.org/10.1177/14759217251333066 SN - 1741-3168 SP - 1 EP - 19 PB - Sage Publications AN - OPUS4-63157 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Held, Mathias A1 - Bulling, Jannis A1 - Lugovtsova, Yevgeniya A1 - Prager, Jens T1 - Determination of isotropic elastic constants from dispersion images based on ultrasonic guided waves by using neural networks N2 - 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. KW - Ultrasonic guided waves KW - Dispersion KW - Elastic constants KW - Neural networks KW - Image processing KW - Vision transformer PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607090 DO - https://doi.org/10.1016/j.ultras.2024.107403 SN - 0041-624X VL - 143 SP - 1 EP - 48 PB - Elsevier B.V. AN - OPUS4-60709 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nicolai, Marcel A1 - Bulling, Jannis A1 - Narayanan, M.M. A1 - Zeipert, Henning A1 - Prager, Jens A1 - Henning, Bernd T1 - Dynamic interface behavior in coupled plates: Investigating Lamb wave mode repulsion with a spring-based model N2 - This study investigates the phenomenon of mode repulsion in Lamb waves propagating through two coupled plates with an elastic interface. Using a spring-based coupling model and the Scaled Boundary Finite Element Method, the dispersion curves of the coupled system are analyzed under various interface conditions—weak coupling, sliding boundary, and perfect coupling. This research highlights how the mechanical stiffness of the interface influences the separation of modes and the emergence of repulsion regions. A novel focus on interface displacements reveals a unique dynamic behavior within the repulsion regions, driven by in-phase and out-of-phase oscillations of the coupled plates. The findings provide a physically grounded explanation of mode repulsion, linking it to the strain patterns in the interface. This insight lays the theoretical groundwork for future applications in material characterization and non-destructive evaluation, enabling more precise selection of Lamb wave modes for scientific and industrial purposes. KW - Lamb waves KW - Mode repulsion KW - Coupled plates KW - Elastic interface KW - Dispersion curves PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642245 DO - https://doi.org/10.1016/j.ultras.2025.107799 SN - 0041-624X VL - 158 SP - 1 EP - 9 PB - Elsevier BV AN - OPUS4-64224 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data N2 - In this paper, we report on a sensor network for structural health monitoring (SHM) of Type IV composite overwrapped pressure vessels (COPVs) designed for hydrogen storage. The sensor network consists of three different SHM sensing technologies: ultrasonic guided waves (GW), acoustic emission (AE) testing, and distributed fiber optic sensors (DFOS). We present an experimental setup for a lifetime test, where a COPV is subjected to cyclic loading. Data from all sensors are collected and centrally evaluated. The COPV failed after approximately 60,000 load cycles, and the sensor network proved capable of detecting and localizing the damage even before the failure of the COPV. This multi-sensor approach offers significantly more channels of 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 not only has a positive effect on operational c KW - Ffiber optic sensors KW - Acoustic emission KW - Guided waves KW - Hydrogen KW - Digital twin KW - Structural health monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-618948 DO - https://doi.org/10.58286/30513 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-61894 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling N2 - 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. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Acoustic emission KW - Ultrasonic guided waves KW - Fiber optic sensors KW - Hydrogen KW - Pressure vessels KW - Structural health monitoring KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602764 UR - https://www.ndt.net/search/docs.php3?id=29702 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-60276 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Duffner, Eric A1 - Ulbricht, Alexander A1 - Prager, Jens A1 - Krebber, Katerina T1 - Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing N2 - 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. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Fiber optic sensors KW - Composites KW - Machine learning KW - Structural health monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602731 UR - https://www.ndt.net/search/docs.php3?id=29701 SP - 1 EP - 7 PB - NDT.net AN - OPUS4-60273 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Heimann, Jan A1 - Mustapha, Samir A1 - Yilmaz, Bengisu A1 - Prager, Jens T1 - Guided Waves in Composite Overwrapped Pressure Vessels and Considerations for Sensor Placement Toward Structural Health Monitoring—An Experimental Study N2 - The utilization of composite overwrapped pressure vessels (COPVs) to store hydrogen, especially at high pressures, is gaining more popularity due to their lightweight design and high storage density, offering significant economic advantages. However, the presence of material defects or fatigue can lead to critical failures, requiring an innovative and robust approach to ensure safe operation and system integrity. Developing a continuous structural health monitoring (SHM) system for COPVs can provide comprehensive realtime information about their condition, facilitating a shift away from periodic inspections. This study scrutinizes the behavior of guided waves (GWs) within COPVs to design a sensor ,network for damage detection and localization. First, the dispersive and multimodal propagation behavior of GWs is experimentally investigated. Subsequently, important parameters for the network design are derived and finally a sensor network consisting of 15 piezoelectric transducers is designed to cover the entire cylindrical area. The effectiveness is then evaluated experimentally by placing artificial defects on the surface of the COPV. The multi-layered dataset of GW signals was analyzed using both commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as statistical features (kurtosis, skewness, variance, etc.). These features were utilized to compute a damage index, and the effectiveness of the detection performance was assessed using receiver operating characteristic curves. It can be seen that some features are more sensitive and robust under varying experimental conditions. The results show that ultrasonic GW SHM system is a promising solution for damage detection and localization in COPVs. KW - Composite Overwrapped Pressure Vessels KW - Guided Waves KW - Structural Health Monitoring KW - Sensors Placement KW - Damage Detection KW - Damage Localization KW - Elastic Wave KW - Testing Methodologies KW - Ultrasonics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625239 DO - https://doi.org/10.1115/1.4067667 SN - 2572-3898 VL - 8 IS - 3 SP - 031007-1 EP - 031007-13 PB - The American Society of Mechanical Engineers (ASME) CY - New York, USA AN - OPUS4-62523 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -