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10.1007/s11356-025-36931-1 http://scihub22266oqcxt.onion/10.1007/s11356-025-36931-1 41108458!ä!41108458 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41108458&cmd=llinks): Failed to open stream: HTTP request failed! 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Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype |pdf=|usr=}}{{41108458}} gab.com Text Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype JO: Environ Sci Pollut Res Int http://www.kidney.de/pget.php?&tw=1&pmid=41108458 #FOAvip 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. Twit Text FOAVip Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype ????Environ Sci Pollut Res Int http://www.kidney.de/pget.php?&tw=1&pmid=41108458 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41108458 #FOAvip English Wikipedia <ref>{{Cite pmid|41108458}}</ref> Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype #MMPMID41108458 Chafaa K; Guetarni IHM; Aissani N; Guirao JLG Environ Sci Pollut Res Int 2025[Oct]; ä (ä): ä PMID41108458show ga 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. äReliability engineering for safety prognostic using bayesian approach:(epmc).pdf DeepDyve Pubget Overpricing