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An abnormal situation modeling method to assist operators in safety-critical systems

Author

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  • Naderpour, Mohsen
  • Lu, Jie
  • Zhang, Guangquan

Abstract

One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.

Suggested Citation

  • Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2015. "An abnormal situation modeling method to assist operators in safety-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 33-47.
    • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:33-47
    DOI: 10.1016/j.ress.2014.08.003
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    References listed on IDEAS

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    1. Ahmet Oztekin & James T. Luxhøj, 2011. "Complex Risk and Uncertainty Modeling for Emergent Aviation Systems: An Application," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Safety and Risk Modeling and Its Applications, pages 263-298, Springer.
    2. Kim, Man Cheol & Seong, Poong Hyun, 2006. "An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 270-282.
    3. Ha, Jun Su & Seong, Poong Hyun, 2009. "A human–machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1557-1567.
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    Cited by:

    1. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
    2. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2016. "A safety-critical decision support system evaluation using situation awareness and workload measures," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 147-159.
    5. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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