Analysis and Estimation of Human Errors From Major Accident Investigation Reports

verfasst von

Caroline Morais, Raphael Moura, Michael Beer, Edoardo Patelli

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

The University of Liverpool
Agency for Petroleum, Natural Gas and Biofuels (ANP)
Tongji University

Typ

Artikel

Journal

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Band

6

Anzahl der Seiten

16

ISSN

2332-9017

Publikationsdatum

03.2020

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Maschinenbau, Sicherheit, Risiko, Zuverlässigkeit und Qualität, Sicherheitsforschung

Elektronische Version(en)

https://doi.org/10.1115/1.4044796 (Zugang: Geschlossen)