Estimation of failure rates provides a key input to quantitative risk assessment (QRA) quantification. International functional safety standard such as BS EN 61511 specifies use of realistic and credible failure data in failure probability analysis. In traditional reliability assessment, mean time to failure is one of the most common approaches to field failure data analysis. Unfortunately, new technology, such as hydrogen failure data is extremely limited. One possible way is to use surrogate failure data from other settings such as commercial nuclear power plants, chemical plants, and offshore oil and natural gas platforms. The proposed Bayesian framework addresses the requirements by allowing industry knowledge about failure rates to be incorporated in a prior gamma distribution and periodic updating process with new survival data as it becomes available. Monte Carlo simulation is adopted which make it practical to solve uncertainty in the failure rate estimation and update these models with many trials in seconds. The result shows that the process of updating failure rate with more samples of new observations and modelling failure data uncertainty using Monte Carlo simulation can be effective in improving reliability quantifications in the existing BS EN 61511 standard.