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Digital twins in safety analysis, risk assessment and emergency management

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  • Zio, Enrico
  • Miqueles, Leonardo

Abstract

Digital twins (DTs) represent an emerging technology that is currently leveraging the monitoring of complex systems, the implementation of autonomous control systems, and assistance during accidents and emergencies in real time. However, aspects such as safety, cybersecurity and reliability of DTs are still open issues that have not been comprehensively addressed. These aspects can offer new insights to evaluate the risk and return obtained from the implementation of DTs. This paper presents a systematic literature review of DTs focused on their use in safety analysis, risk assessment and emergency management. The aim of this work is twofold: (i) to point at the latest advancements in this technology by presenting a catalog of expected functions and twinning enabling technologies in the application domains of interest; and (ii) to point at the limitations and pending challenges on the implementation of DTs for safety analysis, risk assessment and emergency management.

Suggested Citation

  • Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:reensy:v:246:y:2024:i:c:s0951832024001157
    DOI: 10.1016/j.ress.2024.110040
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    References listed on IDEAS

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    1. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. He, Wenbin & Mao, Jianxu & Song, Kai & Li, Zhe & Su, Yulong & Wang, Yaonan & Pan, Xiangcheng, 2023. "Structural performance prediction based on the digital twin model: A battery bracket example," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    4. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Tianran Han & Jianming Zhao & Wenquan Li, 2020. "Smart-Guided Pedestrian Emergency Evacuation in Slender-Shape Infrastructure with Digital Twin Simulations," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    6. Xia, Pengcheng & Huang, Yixiang & Tao, Zhiyu & Liu, Chengliang & Liu, Jie, 2023. "A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Kim, Wongon & Lee, Guesuk & Son, Hyejeong & Choi, Hyunhee & Youn, Byeng D., 2022. "Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Brendan Kochunas & Xun Huan, 2021. "Digital Twin Concepts with Uncertainty for Nuclear Power Applications," Energies, MDPI, vol. 14(14), pages 1-32, July.
    10. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    11. Zhang, Yongchao & Ji, J.C. & Ren, Zhaohui & Ni, Qing & Gu, Fengshou & Feng, Ke & Yu, Kun & Ge, Jian & Lei, Zihao & Liu, Zheng, 2023. "Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. VanDerHorn, Eric & Wang, Zhenghua & Mahadevan, Sankaran, 2022. "Towards a digital twin approach for vessel-specific fatigue damage monitoring and prognosis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    1. Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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