Human reliability analysis—accounting for human actions and external factors through the project life cycle

Abstract

Airplanes, ships, nuclear power plants and chemical production plants (including oil & gas facilities) are examples of industries that depend upon the interaction between operators and machines. Consequently, to assess the risks of those systems, not only the reliability of the technological components has to be accounted for, but also the ‘human model’. For this reason, engineers have been working together with psychologists and sociologists to understand cognitive functions and how the organisational context influences individual actions. Human Reliability Analysis (HRA) identifies and analyses the causes, consequences and contributions of human performance (including failures) in complex sociotechnical systems. Generally, HRA research is concentrated in modelling workers’ performance in the “sharp-end”, assessing the ones directly involved in handling the system, especially operators. However, in theory, a reliability analysis can be applied to any kind of human action, including those from designers and managers. This research will evaluate a way of conducting HRA in the design process, as previous research has demonstrated that design failure is the predominant contributor to human errors (Moura et al., 2016). Bayesian Network (BN) – a systematic way of learning from experience and incorporating new evidence (deterministic or probabilistic) – is proposed to model the complex relationships within cognitive functions, organisational and technological factors. Conditional probability tables have been obtained from a dataset of major accidents from different industry sectors (Moura et al. 2017), using a classification scheme developed by Hollnagel (1998) for an HRA method called CREAM – Cognitive Reliability and Error Analysis Method. The model allows to infer which factors most influence human performance in different scenarios. Also, we will discuss if the model can be applied to any human actions through the project life cycle— since the design phase to the operational phase, including their management.