Human reliability analysis studies from simulator experiments using Bayesian inference

• Published in: Reliability engineering & system safety 2023-01, Vol.229, p.108846, Article 108846
• Main Authors: Garg, Vipul, Vinod, Gopika, Prasad, Mahendra, Chattopadhyay, J., Smith, Curtis, Kant, Vivek
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bayesian Inference; Context; Empirical analysis; Estimates; Human error; Human Reliability Analysis; Nuclear safety; Operator performance; Probabilistic inference; Probabilistic Safety Assessment; Reliability analysis; Reliability engineering; Simulation; Statistical analysis; Statistical inference
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108846

Probabilistic Safety Assessment (PSA) of complex facilities is performed to arrive at the risk posed by them. PSA also accounts for the contribution of the human errors towards the overall risk through Human Reliability Analysis (HRA) in terms of Human Error Probability (HEP). Human operators are part of the system and do not work in isolation. Their performance is influenced by the context in which the actions are performed. As a result, quantification of HEP requires operator performance data under the given context. Some good sources of operator performance data are plant's operation data, simulator data and expert judgement. The plant operation data pertaining to HRA is generally sparse. In this situation, a full scope plant simulator provides a good alternative for operator performance data generation. Many of the currently practised HRA methods have been developed by combining the empirical evidence with expert judgement and contain a lot of uncertainty in their estimates. Bayesian inference is suitable for updating the prior HRA estimates with the simulator evidence to obtain the posterior HEP. In this study, posterior HEP has been calculated for postulated accident scenarios in advanced reactor (first of its kind) at design stage, using plant simulator.