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 - 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 - 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 -