Analysis and Estimation of Human Errors From Major Accident Investigation Reports

Abstract

Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented.

Machine-Learning Tool for Human Factors Evaluation – Application to Lion Air Boeing 737-8 Max Accident

“…To build a model of human error in complex industries the Bayesian networks proposed by by some of the authors has been used [6]. Bayesian Networks are a probabilistic tool that can be presented in the form of a directed acyclic graph made of nodes (variables) connected by links.…”

“…In [6], to build the structure of the Bayesian network the authors have used the dependency among the variables proposed in [8]. This arrangement of parents and children nodes connected by links allows predictive and diagnostic calculations.…”

“…However, human behaviour is highly variable and depends not only on the individual but also on the organizational and technological factorsall of them sources of aleatoric and epistemic uncertainties. To obtain these probabilities optimizing the 'learning from incidents' concept, Morais et al have developed an approach that uses human factors data from major accidents and a probabilistic tool that accommodates those uncertainties [6]. Bayesian networks were chosen to model human errors due to the possibility of updating the model and its outputs with new evidence for each new accident report that is issued [7].…”

Application of the Structure Function in the Evaluation of the Human Factor in Healthcare

“…Mathematical representations map component states into system states and are used to compute reliability indices and measures for system evaluations. There are many types of mathematical representations, such as fault trees [3], reliability block diagrams [4], structure functions [4,5], Markov models [6], Petri nets [7], Bayesan belief networks [8,9], credal networks [10,11], survival signatures [12] and others. The choice and use one of these representations depends on application problems and the specifics of risk/reliability analysis.…”

“…Mathematical representations in the form of a fault tree, reliability block diagram or structure function re more suitable for systems in which the causal implications of the failure/success of a component is well identified and deterministic. Advantages of these mathematical representations are simplicity and the possibility to be constructed for a system of any structure complexity [13], but new methods need to be developed for their application in time-dependent reliability analysis, such using a survival signature [12,14], credal network [11,15] or other method [16]. Markov models or Monte Carlo simulations can be accepted for time-depend (dynamic) analysis of system reliability [6,15].…”