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    <title language="eng">Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling</title>
    <abstract language="eng">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.</abstract>
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    <author>Christos Karapanagiotis</author>
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      <value>Hydrogen</value>
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      <language>eng</language>
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      <value>Machine learning</value>
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      <language>eng</language>
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      <value>Pressure vessels</value>
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      <language>eng</language>
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      <value>Structural health monitoring</value>
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    <id>60275</id>
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    <title language="eng">Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing</title>
    <abstract language="eng">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.</abstract>
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    <author>Christos Karapanagiotis</author>
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      <language>eng</language>
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      <value>Hydrogen</value>
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    <subject>
      <language>eng</language>
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      <value>Composites</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pressure vessels</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fiber optic sensors</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
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    <title language="eng">Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of the 11th European Workshop on Structural Health Monitoring</parentTitle>
    <identifier type="url">https://www.ndt.net/search/docs.php3?id=29702</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-602764</identifier>
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    <author>Christos Karapanagiotis</author>
    <author>Jan Heimann</author>
    <author>Eric Duffner</author>
    <author>Amir Charmi</author>
    <author>Marcus Schukar</author>
    <author>Seyedreza Hashemi</author>
    <author>Jens Prager</author>
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      <language>eng</language>
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      <value>Acoustic emission</value>
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      <language>eng</language>
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      <value>Fiber optic sensors</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hydrogen</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pressure vessels</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
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    <id>62764</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <pageFirst>2</pageFirst>
    <pageLast>10</pageLast>
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    <issue>2</issue>
    <volume>7</volume>
    <type>article</type>
    <publisherName>IOP Publishing Ltd</publisherName>
    <publisherPlace>Bristol, UK</publisherPlace>
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    <title language="eng">Real-time monitoring of hydrogen composite pressure vessels using surface-applied distributed fiber optic sensors</title>
    <abstract language="eng">In this paper, we report to the best of our knowledge for the first time on continuous real-time monitoring of composite overwrapped pressure vessels (COPVs) designed for hydrogen storage using surface-applied distributed fiber optic sensors (DFOS). We conducted continuous and real-time DFOS measurements during pressure cycling tests consisting of periodic pressure fluctuations between 20 bar and 875 bar, with a rate of 5 cycles min−1. During pressure cycling, the DFOS system measured strain changes, that under normal operating conditions were linearly correlated to changes in pressure. To detect and quantify damage-related anomalies, we trained a simple regression model to predict strain from pressure data and used the difference between predicted and measured values as a damage indicator. With our approach, the DFOS system not only detected and localized the damage but also continuously tracked its evolution in real time under dynamic pressure conditions. Furthermore, unlike previous studies where optical fibers were embedded within the composite structure, we applied them on the COPV surface, reducing both implementation cost and time while eliminating the need to modify the COPV manufacturing process. Based on our results, we are confident that DFOS can enhance safety and facilitate the transition from time-consuming periodic inspections to more efficient, machine learning-based predictive maintenance.</abstract>
    <parentTitle language="eng">Journal of Physics: Photonics</parentTitle>
    <identifier type="issn">2515-7647</identifier>
    <identifier type="doi">10.1088/2515-7647/adb9ac</identifier>
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    <enrichment key="date_peer_review">02.04.2025</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christos Karapanagiotis</author>
    <author>Mathias Breithaupt</author>
    <author>Eric Duffner</author>
    <author>Marcus Schukar</author>
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      <language>eng</language>
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      <value>Fiber optic sensors</value>
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      <language>eng</language>
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      <language>eng</language>
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      <value>Predictive maintenance</value>
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