Bayesian network-based human error reliability assessment of derailments

Dindar, S; Sakdirat, K; An, M

Abstract

The knowledge acquired in relation to failures associated with components has made significant contributions to the development of components with increased reliability, as well as a reduction in the number of rail incidents caused by certain system defects. These new systems have led to innovative developments in both the operations and technology of rail networks. Hence, rail employees must now function in conditions that have high complexity that are hard to comprehend. The risk of failure caused by human error (such as by dispatchers, train crews and track engineers) has developed into a significant safety problem. This study is the world first to provide novel insights into better understanding human errors, which result in derailments at rail turnouts. A most- to-least-critical importance ranking of these errors is established throughout a novel risk management technique. Moreover, the new findings and recommendations of this research study have a strong potential for industry to improve the reliability of rail operation, and avoid safety concerns regarding train derailments at rail turnouts.

Citation

Dindar, S., Sakdirat, K., & An, M. (2020). Bayesian network-based human error reliability assessment of derailments. Reliability Engineering and System Safety, 197, 106825. https://doi.org/10.1016/j.ress.2020.106825

• Journal Article Type: Article
• Acceptance Date: Jan 19, 2020
• Online Publication Date: Jan 28, 2020
• Publication Date: May 1, 2020
• Deposit Date: Apr 18, 2020
• Publicly Available Date: Jan 28, 2021
• Journal: Reliability Engineering & System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Volume: 197
• Pages: 106825
• DOI: https://doi.org/10.1016/j.ress.2020.106825
• Publisher URL: https://doi.org/10.1016/j.ress.2020.106825
• Related Public URLs: https://www.journals.elsevier.com/reliability-engineering-and-system-safety
• Additional Information: Projects : Technology Research Innovations Grant Scheme;RISEN: Rail Infrastructure Systems Engineering Network; Grant Number: RCS15/0233; Grant Number: 691135