A hybrid HEART method to estimate human error probabilities in locomotive driving process

Jian-Lan Zhou, Yi Lei and Yang Chen

Reliability Engineering and System Safety, 2019, vol. 188, issue C, 80-89

Abstract: Human reliability assessment is an essential work to guarantee the safety of locomotive driving process. Human Error Assessment and Reduction Technique (HEART) is a well-known approach applied to determine human error probability (HEP). However, the deficiencies of HEART are that the list of Error-producing conditions does not include many relevant railway operating performance shaping factors, and HEART does not provide the practitioners with a concrete method to determine the assessed proportion of affect (APOA), which force a heavy reliance on the judgement of single rater in the field. To overcome this problem and to obtain a more accurate APOA, we propose a hybrid HEART method which utilizes the evidence theory to fuse raters’ opinions to EPCs determination and APOA for each corresponding EPC and quantify the subjective judgment. A complete locomotive driving process is performed to evaluate HEP. Finally, we apply Monte Carlo simulation to obtain system reliability and validate proposed method. The calculated results are consistent with the experience and knowledge of safety management and simulation results. This hybrid HEART approach is useful to reduce the likelihood of occurrence of errors, and improve the overall safety level in locomotive driving operation and other industries.

Keywords: Railway system; Human error probability; Human error assessment and reduction technique; Multi-source information fusion; Fault tree analysis; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:188:y:2019:i:c:p:80-89

DOI: 10.1016/j.ress.2019.03.001

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