Open Access

ARTICLE

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

Xiaoyan Su^1,*, Shuwen Shang¹, Zhihui Xu², Hong Qian¹, Xiaolei Pan¹

1 School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2 State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, Shenzhen, 518172, China

* Corresponding Author: Xiaoyan Su. Email:

(This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)

Computer Modeling in Engineering & Sciences 2024, 138(2), 1813-1826. https://doi.org/10.32604/cmes.2023.030957

Received 06 March 2023; Accepted 20 June 2023; Issue published 17 November 2023

Abstract

With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient. First, the dependence between every two PSFs is measured by the Pearson correlation coefficient. Second, the weights of the PSFs are obtained by considering the total dependence degree. Finally, PSFs’ multipliers are modified based on the weights of corresponding PSFs, and then used in the calculating of human error probability (HEP). A case study is used to illustrate the procedure and effectiveness of the proposed method.

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