13 May Human Reliability
More and more researchers in Human Reliability Analysis (HRA) tend to think it needs to be an integration of different disciplines such as human factors, social psychology, behavioral science and organizational management, and so on (LaSala, 1998; He and Huang, 2007). HRA presents a comprehensive description of human error analysis and reliability evaluation from qualitative and quantitative attributes (Boring, 2007). Discussions on independent, coherent and complex behavior can be applied to physical, biological or social systems. The discussion of independent, coherent and complex behavior can be applied to physical, biological or social systems. The complexity profile of human beings is shown by the range of behaviors exhibited on the relevant behavioral scales.
Over the past few decades, different HRA methods have been proposed to analyze, predict and minimize human error in nuclear power plants and other process industries. These include THERP (Technique for Human Error Rate Prediction), CREAM (Cognitive Reliability and Error Analysis Method), and ASP/SPAR (Accident Sequence Precursor/Standardized Plant Analysis Risk Model) (Swain and Guttmann, 1983; Hollnagel, 1998; Gertman et al., 2004; Chang and Lois, 2012; Groth et al., 2014). Recent HRA approaches highlight basic cognitive mechanisms and causes of human errors from the point of view of psychology, cognition and neuroscience, making possible a more rational and more persuasive qualitative analysis and providing more dynamic structure for quantitative analysis (Zhan et al., 2019).
The second generation Human Reliability Analysis (HRA) methods such as the Cognitive Reliability and Error Analysis Method (CREAM) (Hollnagel, 1998) were used to proactively assess the erroneous human actions in complicated systems in a way that the context influencing human action is appropriately taken into account (Yang et al., 2019).
- Boring, R.L. (2007). “Dynamic human reliability analysis: Benefits and challenges of simulating human performance”. risk, Reliab. Soc. Saf. 2, 1043–1049.
- Chang, J., Lois, E. (2012). “Overview of the NRC’s HRA data program and current activities, in: Proceedings of 11th Probabilistic Safety Assessment and Management European Safety and Reliability”. pp. 25-29.
- Gertman, D., Blackman, H., Marble, J., Byers, J., Smith, C. (2004). “The SPAR-H Human Reliability Analysis Method”. (NUREG/CR-6883), Washington D.C: US Nuclear Regulatory Commission.
- Groth, K.M., Smith, C.L., Swiler, L.P. (2014). “A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods”. Reliab. Eng. Syst. Saf. 128, 32–40.
- He, X., Huang, X. (2007). “Human Reliability Analysis in Industrial Systems: Theory, Methods and Practice”. Tsinghua Press, Beijing.
- Hollnagel, E. (1998). “Cognitive Reliability and Error Analysis Method – CREAM”. Elsevier Science, Oxford, UK.
- LaSala, K.P. (1998). “Human performance reliability: a historical perspective, IEEE Trans. Reliab”. 47 (3), 365–371.
- Swain, A.D., Guttmann, H.E. (1983). “Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications, Final Report”. NUREG/CR-1278, US Nuclear Regulatory Commission, Washington, DC.
- Yang, z., Abujaafar, KH. M., Qu, ZH., Wang, J., Nazir, S. (2019). “Use of evidential reasoning for eliciting bayesian subjective probabilities in human reliability analysis: A maritime case”. Ocean Engineering 186, 106095.
- Zhan, Y., R. Tadikamalla, P., A. Craft, J., Lu, J., Yuan, J., Pei, ZH., Li, SH. (2019). “Human reliability study on the door operation from the view of Deep Machine Learning”. Future Generation Computer Systems, 99, 149-153.