A novel integrated methodology for human reliability assessment in hydrogen fuelling stations

Abstract

Hydrogen fuel cell vehicles have proliferated in recent years, leading to a subsequent increase in hydrogen fuelling stations (HFSs). Operation and maintenance activities of an HFS prone to undesired events caused by human errors. Estimating and evaluating the risk associated with operators' performance can help to enhance the safety of such operations. The human error assessment and reduction technique (HEART) methodology has been widely used to assess the reliability of human performance. However, this technique is not entirely reliable since it depends on experts' experience and judgment, and the model's final outcome is subject to uncertainty. Besides, the HEART method ignores the dependencies of all the tasks required to assess human error probability (HEP). This paper presents a new integrated methodology for quantifying the impacts of different conditions on the reliability of operators' performances in HFSs. The proposed framework is constructed based on the Bayesian network (BN) for complimenting the application of HEART to deal with the lack of data and experts' judgment. Hence, it reduces the inaccuracy of HEP assessed by HEART by plotting it to a BN and considering the dependencies of all involved tasks. Subsequently, the best-worst method (BWM) is utilised to deal with the subjectivity of expert judgment. The integrated BN-BWM-HEART technique enhances the safety and reliability of the current operation and maintenance practices of an HFS. The results indicate that the integrated BN-BWM-HEART technique has the potential capacity to reduce the uncertainty associated with HEP estimation. So, the outcome of the developed methodology enhances the safety and reliability of operations and maintenance activities in an HFS.