TY - CONF A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Mustapha, Samir A1 - Charmi, Amir A1 - Prager, Jens T1 - Guided wave ultrasonic features in composite overwrapped pressure vessels towards 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 - European Conference on Non-Destructive Testing (ECNDT) 2023 CY - Lisbon, Portugal DA - 03.07.2023 KW - NDT4.0 KW - CComposite overwrapped pressure vessels KW - Structural health monitoring KW - Ultrasonic guided waves KW - Predictive maintenance PY - 2023 AN - OPUS4-57950 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 - 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 - Heimann, Jan A1 - Yilmaz, Bengisu A1 - Charmi, Amir A1 - Duffner, Eric A1 - Schukar, Marcus A1 - Prager, Jens T1 - Structural Health Monitoring (SHM) for continuous monitoring of hydrogen pressure vessels N2 - While hydrogen is one of the most promising energy carriers, the safety of hydrogen storage technology remains one of the most important factors for technological and societal approval. While the engineering safety factors of the pressure vessels are kept high, the periodic inspection and the limited lifetime are making the application very costly considering manpower, time, money, and material waste. The development of an integrated structural health monitoring system can allow an easy transition from the current situation to cost-effective predictive maintenance. Hence, we propose to integrate three different SHM systems into hydrogen pressure vessels, namely guided wave ultrasonics, acoustic emission, and fibre optic sensing, to continuously monitor the condition and integrity. In this work, we evaluated the condition of a Type IV composite overwrapped pressure vessel using ultrasonic guided wave propagation. We mounted fifteen piezo-electric wafers on the composite cylinder by shaping three rings containing five sensors each. We acquired data from the sensor network following different boundary conditions with artificial damages on the selected locations. The data were evaluated with guided wave tomography techniques using ultrasonic features (amplitude, frequency, etc.) as well as artificial intelligence (AI). The results suggest that both traditional guided wave fusion techniques and AI-based characterization methods can detect artificial damages. In future work, it is planned to integrate acoustic emission and fibre optic sensing. Moreover, the measurement and the test results will be implemented into a digital twin to derive trends and make predictions on the damage propagation as well as the remaining useful lifetime. This work has received funding from German Ministry of Economic Affairs and Climate Actions within the QI-Digital initiative (www.qi-digital.de). T2 - SCHALL 23 CY - Wetzlar, Germany DA - 21.03.2023 KW - Structural Health Monitoring KW - Ultrasonic Guided Waves KW - Composite Overwrapped Pressure Vessel KW - Hydrogen PY - 2023 AN - OPUS4-58026 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heimann, Jan A1 - Mustapha, S. A1 - Yilmaz, Bengisu A1 - Charmi, Amir A1 - Brence, Blaž A1 - Prager, Jens T1 - Untersuchung der Ausbreitung von geführten Ultraschallwellen in Wasserstoffdruckbehältern zur Zustandsüberwachung N2 - Die zunehmende Bedeutung von Wasserstoff als emissionsfreier Energieträger der Zukunft lässt die Anforderungen an eine technisch einwandfreie und sichere Wasserstoffspeicherung steigen. Im Mobilitätssektor kommen dabei vorwiegend Kohlefaserverbundbehälter zur Speicherung von gasförmigem Wasserstoff im Hochdruckbereich zum Einsatz, die sich durch ihre Leichtbauweise bei gleichzeitig hoher Speicherkapazität auszeichnen. Materialfehler oder -ermüdung können jedoch zum Ausfall bis hin zum kritischen Versagensfall führen. Ein sicherer Betrieb der Behälter erfordert daher ein innovatives und zuverlässiges Konzept, um deren Integrität zu gewährleisten und folgenschwere Zwischenfälle zu vermeiden. Die Strukturüberwachung mittels geführter Ultraschallwellen ist dafür einer der prominentesten Ansätze, da sich die Wellen über große Entfernungen in der Struktur ausbreiten können und zudem sehr empfindlich auf kleinste Materialdefekte reagieren. In diesem Beitrag wird der Aufbau eines Sensornetzwerks zur Schadenserkennung und -lokalisierung vorgestellt, das auf den Prinzipien der Ausbreitung geführter Ultraschallwellen in Druckbehältern aus Verbundwerkstoffen basiert. Dazu werden in einem ersten Schritt das dispersive und multimodale Ausbreitungsverhalten analysiert und dominante Wellenmoden identifiziert. Basierend auf der Analyse werden Dämpfungsverhalten und Empfindlichkeit gegenüber künstlichen Defekten bestimmt. Unter Verwendung der ermittelten Informationen wird ein Sensornetzwerk bestehend aus piezoelektrischen Flächenwandlern entworfen, welches den zu untersuchenden Bereich vollständig abdecken soll. Das Ergebnis wird anschließend durch Aufbringen künstlicher Defekte experimentell evaluiert und präsentiert. T2 - DAGA 2023 CY - Hamburg, Germany DA - 06.03.2023 KW - Geführte Ultraschallwellen KW - Zustandsüberwachung KW - Wasserstoffdruckbehälter KW - Sensornetzwerk KW - Structural Health Monitoring PY - 2023 UR - https://pub.dega-akustik.de/DAGA_2023 SN - 978-3-939296-21-8 SP - 1598 EP - 1601 CY - Berlin AN - OPUS4-58022 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heimann, Jan A1 - Charmi, Amir A1 - Karapanagiotis, Christos A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - How structural health monitoring can be embedded in a digital quality infrastructure: an example. N2 - The digital Quality Infrastructure (QI) initiative “QI-Digital” in Germany is focusing on implementing new technologies and approaches to ensure that the task of quality assurance is more efficient and ready for the digital and green transformation of the economy. The implementation of quality control key elements, such as Smart Standards, Digital Certificates and QI-cloud solutions shall contribute to solving the socio-economic, ecological, and technological challenges of our time. Hydrogen is a key energy carrier and has the potential to play a significant role in the energy transition, especially in mobility. An essential factor for the broad acceptance of hydrogen-based mobility is the availability of refueling stations that operate reliably and safely. Using the example of a Hydrogen Refueling Station (HRS) built within the QI-Digital initiative, the Federal Institute for Material Research and Testing (BAM) aims to establish a real laboratory where modern measurement techniques and new digital methods are implemented to enhance operational safety, availability, and economic efficiency and render the technology more attractive for the industry. In this work, we present an approach to establish a Structural Health Monitoring (SHM) system on a high-pressure buffer inside HRS and show how it could be embedded into a digital QI. The high-pressure buffers are essential components of the plant which are currently inspected periodically without regard to their operating history. Focusing on the transition to a continuous and digitally supported monitoring of the component’s integrity during operation the novel inspection scheme will be linked to a completely digitalized component-related documentation and tested using digital certificates. This allows the operational safety and, if necessary, the remaining useful lifetime to be assessed on an ongoing basis and to be a valuable contribution to increasing sustainability. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Structural health monitoring KW - Quality infrastructure KW - Digital transformation KW - Reliability KW - Quality control KW - Industry 4.0 PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607109 DO - https://doi.org/10.58286/29769 SN - 1435-4934 SP - 1 EP - 7 PB - NDT.net AN - OPUS4-60710 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hashemi, Seyedreza A1 - Heimann, Jan A1 - Charmi, Amir A1 - Prager, Jens T1 - Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading N2 - The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility. The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Predictive Maintenance KW - Industry 4.0 KW - Structural Health Monitoring KW - Ultrasonic Guided Waves KW - Hydrogen Storage KW - Type IV Composite Overwrapped Pressure Vessels PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-608600 DO - https://doi.org/10.58286/29780 SN - 1435-4934 SP - 1 EP - 8 PB - NDT.net CY - Potsdam, Germany AN - OPUS4-60860 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 - 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 -