With the emerging market for hydrogen technologies, there is a need for more operational, testing, and maintenance data available to inform and prioritize component improvements, guide system design, and support safe deployment [1]. This is especially important in hydrogen systems because of the presence of unique system architectures, specialized component designs, and the distinct thermophysical properties of hydrogen, particularly in the cryogenic liquid hydrogen used in high demand fueling stations [2]. Statistically based reliability analyses often rely on large datasets, and limited data challenges the use of traditional techniques to analyze hydrogen components and systems. To address the challenge of analyzing limited data from multiple sources, we use Bayesian methods, which explicitly express prior knowledge and multiple forms of data in the formulation of probability. While Bayesian approaches have been historically limited due to computational demands, modern numerical techniques, such as Markov Chain Monte Carlo (MCMC), have made these solution methods practical for complex reliability applications [3, 4]. In this work, we perform a Bayesian reliability analysis on the failure of cryogenic reciprocating pump cold ends, to get better reliability models for hydrogen fueling stations. Bayesian updating with MCMC is applied to cold end failure data obtained from maintenance records to estimate reliability model parameters under conditions of limited data availability. The resulting models characterize failure behavior using a competing risks framework, where each failure mode is represented by an independent probabilistic distribution, while explicitly integrating additional information and quantifying uncertainty due to limited data. The framework also supports sequential Bayesian updating, allowing for new data to be incorporated as it becomes available. These results establish credible bounds on cold end reliability and enable the identification of the most frequently occurring failure modes. Our results provide insight into cold end behavior through failure mode specific failure distributions and inform preventative maintenance strategies based on B10 life estimates. Overall, the proposed methodology, estimated failure mode parameters, and maintenance recommendations support the reliable deployment of liquid hydrogen fueling stations.
References:
[1] K. M. Groth, L. M. Reising, V. Grabovetska, and A. Ruiz, “Hydrogen Systems Risk and Reliability Challenges, Priorities, and Workshop Insights,” Hydrogen Safety, vol. 19, no. 1, pp. 88–98, Nov. 2025, doi: 10.58895/hysafe.23.

[2] S. Mosleh, C. Schaad, R. Yang, and K. M. Groth, “A methodology for quantitative risk assessment of a high-capacity hydrogen fueling station with liquid hydrogen storage,” International Journal of Hydrogen Energy, vol. 112, pp. 544–553, Mar. 2025, doi: 10.1016/j.ijhydene.2025.02.169.

[3] J. Lin, “An Integrated Procedure for Bayesian Reliability Inference Using MCMC,” Journal of Quality and Reliability Engineering, vol. 2014, pp. 1–16, Jan. 2014, doi: 10.1155/2014/264920.

[4] Kelly, D., and C. Smith. Bayesian Inference for Probabilistic Risk Assessment: A Practitioner’s Guidebook. Springer Series in Reliability Engineering. Springer London, 2011.