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 - 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 -