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<p>Hydrogen, a future fuel, solves most of the energy challenges and requires robust reliability studies, a domain currently lacking in hydrogen system research. This study aims to improve the safety and efficiency of green hydrogen production by conducting a risk analysis of a prototype system and providing a tool for safety prognosis. Both Fault Tree Analysis and Bayesian network inferences were employed to identify critical components and optimize maintenance strategies. By comparing these probabilistic techniques, system vulnerabilities were successfully identified, and their impact on overall safety was assessed. The unavailability of the system was calculated to be 0.3783 using Fault Tree Analysis and 0.37575 using the Bayesian network, demonstrating an agreement between both methods. Quantitative importance analysis further revealed that the switch had a Marginal Importance Factor (MIF) of 0.938644, and Critical Importance Factor (CIF) = 0.837), regulator 12&#xa0;V (MIF = 0.65273, CIF = 0.0758), and electrolyzer (MIF = 0.63046, CIF = 0.0165) were the most critical components affecting system reliability. Several other parts showed moderate importance, and some had zero-impact factors, such as solar panels, humans, and anemometer, suggesting high reliability or modeling refinements, indicating either high reliability or possible modeling considerations. Qualitatively, these results highlight the effectiveness of combining multiple probabilistic approaches for comprehensive safety assessment, and pinpoint the components most in need of maintenance. The results of this analysis provide valuable insights into advancing research and facilitating the transition to hydrogen as a renewable energy source. These findings will support the development of robust safety protocols and ensure the safe scaling of hydrogen technologies.</p>

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Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Khatima Chafaa,
  • Islam Hadj Mohamed Guetarni,
  • Nassima Aissani,
  • Juan Luis García Guirao

摘要

Hydrogen, a future fuel, solves most of the energy challenges and requires robust reliability studies, a domain currently lacking in hydrogen system research. This study aims to improve the safety and efficiency of green hydrogen production by conducting a risk analysis of a prototype system and providing a tool for safety prognosis. Both Fault Tree Analysis and Bayesian network inferences were employed to identify critical components and optimize maintenance strategies. By comparing these probabilistic techniques, system vulnerabilities were successfully identified, and their impact on overall safety was assessed. The unavailability of the system was calculated to be 0.3783 using Fault Tree Analysis and 0.37575 using the Bayesian network, demonstrating an agreement between both methods. Quantitative importance analysis further revealed that the switch had a Marginal Importance Factor (MIF) of 0.938644, and Critical Importance Factor (CIF) = 0.837), regulator 12 V (MIF = 0.65273, CIF = 0.0758), and electrolyzer (MIF = 0.63046, CIF = 0.0165) were the most critical components affecting system reliability. Several other parts showed moderate importance, and some had zero-impact factors, such as solar panels, humans, and anemometer, suggesting high reliability or modeling refinements, indicating either high reliability or possible modeling considerations. Qualitatively, these results highlight the effectiveness of combining multiple probabilistic approaches for comprehensive safety assessment, and pinpoint the components most in need of maintenance. The results of this analysis provide valuable insights into advancing research and facilitating the transition to hydrogen as a renewable energy source. These findings will support the development of robust safety protocols and ensure the safe scaling of hydrogen technologies.