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Human Error Data Collection and Comparison with Predictions by SPAR‐H

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  • Peng Liu
  • Zhizhong Li

Abstract

There is a scarcity of empirical data on human error for human reliability analysis (HRA). This situation can increase the variability and impair the validity of HRA outcomes in risk analysis. In this work, a microworld study was used to investigate the effects of performance shaping factors (PSFs) and their interrelationships and combined effects on the human error probability (HEP). The PSFs involved were task complexity, time availability, experience, and time pressure. The empirical data obtained were compared with predictions by the Standardized Plant Analysis Risk‐Human Reliability Method (SPAR‐H) and data from other sources. The comparison included three aspects: (1) HEP, (2) relative effects of the PSFs, and (3) error types. Results showed that the HEP decreased with experience and time availability levels. The significant relationship between task complexity and the HEP depended on time availability and experience, and time availability affected the HEP through time pressure. The empirical HEPs were higher than the HEPs predicted by SPAR‐H under different PSF combinations, showing the tendency of SPAR‐H to produce relatively optimistic results in our study. The relative effects of two PSFs (i.e., experience/training and stress/stressors) in SPAR‐H agreed to some extent with those in our study. Several error types agreed well with those from operational experience and a database for nuclear power plants (NPPs).

Suggested Citation

  • Peng Liu & Zhizhong Li, 2014. "Human Error Data Collection and Comparison with Predictions by SPAR‐H," Risk Analysis, John Wiley & Sons, vol. 34(9), pages 1706-1719, September.
  • Handle: RePEc:wly:riskan:v:34:y:2014:i:9:p:1706-1719
    DOI: 10.1111/risa.12199
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    References listed on IDEAS

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    Cited by:

    1. Peter J. Majewicz & Paul Blessner & Bill Olson & Timothy Blackburn, 2020. "Estimating the Probability of Human Error by Incorporating Component Failure Data from User‐Induced Defects in the Development of Complex Electrical Systems," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 200-214, January.
    2. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin, 2020. "Performance shaping factors dependence assessment through moderating and mediating effect analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zhang, Li & Liu, Xueyang & Ding, Qianqiao & Qin, Zhuomin & ÄŒepin, Marko, 2021. "Analysis of dependencies among performance shaping factors in human reliability analysis based on a system dynamics approach," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Shirley, Rachel Benish & Smidts, Carol & Zhao, Yunfei, 2020. "Development of a quantitative Bayesian network mapping objective factors to subjective performance shaping factor evaluations: An example using student operators in a digital nuclear power plant simul," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    5. Kim, Yochan & Park, Jinkyun, 2019. "Incorporating prior knowledge with simulation data to estimate PSF multipliers using Bayesian logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 210-217.
    6. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Liu, Peng & Qiu, Yongping & Hu, Juntao & Tong, Jiejuan & Zhao, Jun & Li, Zhizhong, 2020. "Expert judgments for performance shaping Factors’ multiplier design in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 194(C).

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