Human Reliability Assessment under Uncertainty – Towards a Formal Method

Abstract : Humans are and will remain one of the critical constituents of a technological system. The study of human factors is a broad domain with equally varying applications. Furthermore, with the advent of new technologies in safety-critical systems there is always a need to ensure system safety and reliability in accordance with increasingly demanding certification standards. Human reliability is a cause of concern as hardware becomes increasingly reliable and relatively human error is rising in its share of causing an accident. Human Reliability Analysis (HRA) provides a way to quantify the risk associated with a human. This paper presents a discussion on the development of a HRA model for the domain of transportation, rail transport in particular. Railway specific human factors studies are analyzed to identify safety relevant factors in order to create a relevant and relatively applicable Performance Shaping Factor list. This list of factors is compared with railway specific studies to address domain specific concerns, further augmenting it with quantification levels for each. A discussion on our proposition towards the integration of HRA for obtaining human induced system-level risk taking into account uncertainty in data and current work's positioning in proposed methodology is also included.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01266950
Contributor : Subeer Rangra <>
Submitted on : Wednesday, February 3, 2016 - 3:55:37 PM
Last modification on : Friday, December 7, 2018 - 12:50:03 PM

Links full text

Identifiers

Citation

Subeer Rangra, Mohamed Sallak, Walter Schön, Frédéric Vanderhaegen. Human Reliability Assessment under Uncertainty – Towards a Formal Method. 6th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2015) , Jul 2015, Las Vegas, United States. pp.3230-3237, ⟨10.1016/j.promfg.2015.07.874⟩. ⟨hal-01266950⟩

Share

Metrics

Record views

120