Improving Human Reliability Analysis for Railway Systems Using Fuzzy Logic

No Thumbnail Available
Identifiers
URI: https://hdl.handle.net/11556/3496
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3112527
Publication date
2021
Authors
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert's subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, \alpha -cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach.
Description
Publisher Copyright: © 2013 IEEE.
Keywords
Fuzzy logic , human factors , maintenance , railway engineering , reliability engineering , General Computer Science , General Materials Science , General Engineering
Type
journal article
Citation
Ciani , L , Guidi , G , Patrizi , G & Galar , D 2021 , ' Improving Human Reliability Analysis for Railway Systems Using Fuzzy Logic ' , IEEE Access , vol. 9 , pp. 128648-128662 . https://doi.org/10.1109/ACCESS.2021.3112527