Identifying and clustering performance shaping factors for nuclear power plant commissioning tasks

Yin, Zijian; Liu, Zhaopeng; Li, Zhizhong

doi:10.1002/hfm.20868

Cited by 6 publications

(1 citation statement)

References 63 publications

(163 reference statements)

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“…Nowadays, the application of PSFs has beyond the scope of HRA. There are several other research topics addressing PSFs, including human behavior models, human-related event analysis method and human performance database [31].…”

Section: Introductionmentioning

confidence: 99%

Mental Overload Assessment Method Considering the Effects of Performance Shaping Factors

Guo

Zeng

et al. 2023

IEEE Access

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The assessment of mental overload is essential as mental overload has been proved to be a critical cause of many accidents. Mental overload is affected by various performance shaping factors (PSFs), and harsh PSFs will either increase the task demand or decrease the overload threshold, which is rarely considered in the current mental overload assessment method. This research proposes a VACP-based mental overload assessment model which considers the effects of PSFs. In the VACP model, mental overload can be identified when the sum of the task demand values of every unit task is greater than the given threshold. In human reliability analysis, PSFs are mainly used as weighting factors to modify basic human error probability. In our research, by virtue of the quantitative relationship between human error and mental workload, the weighting factors of PSFs are converted to modify the task demand values and threshold for VACP activities. Furthermore, Bayesian Network (BN) is used to model the influence of PSFs and to calculate the probability of mental overload. The proposed method is applied to an accident involving a helicopter crash that occurred in Maryland, and the results show that, in comparison with the VACP method, the proposed method can more effectively identify the state of mental overload and provide a more rational explanation of the process of the accident.

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Section: Introductionmentioning

confidence: 99%