IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v246y2024ics0951832024001157.html
   My bibliography  Save this article

Digital twins in safety analysis, risk assessment and emergency management

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024001157
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. Brendan Kochunas & Xun Huan, 2021. "Digital Twin Concepts with Uncertainty for Nuclear Power Applications," Energies, MDPI, vol. 14(14), pages 1-32, July.
    8. 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.
    9. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    10. 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).
    11. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Deng, Congying & Deng, Zihao & Miao, Jianguo, 2024. "Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Yan, Shen & Zhong, Xiang & Shao, Haidong & Ming, Yuhang & Liu, Chao & Liu, Bin, 2023. "Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Li, Xin & Li, Shuhua & Wei, Dong & Si, Lei & Yu, Kun & Yan, Ke, 2024. "Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Giannakeas, Ilias N. & Mazaheri, Fatemeh & Bacarreza, Omar & Khodaei, Zahra Sharif & Aliabadi, Ferri M.H., 2023. "Probabilistic residual strength assessment of smart composite aircraft panels using guided waves," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Jian-Guo Duan & Tian-Yu Ma & Qing-Lei Zhang & Zhen Liu & Ji-Yun Qin, 2023. "Design and application of digital twin system for the blade-rotor test rig," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 753-769, February.
    9. Liang, Pengfei & Tian, Jiaye & Wang, Suiyan & Yuan, Xiaoming, 2024. "Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    11. Cong Wang & Lisha Zhao & Shuhong Wu & Xinmin Song, 2020. "Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction," Energies, MDPI, vol. 13(23), pages 1-19, November.
    12. Theresa Liegl & Simon Schramm & Philipp Kuhn & Thomas Hamacher, 2023. "Considering Socio-Technical Parameters in Energy System Models—The Current Status and Next Steps," Energies, MDPI, vol. 16(20), pages 1-19, October.
    13. Kohtz, Sara & Zhao, Junhan & Renteria, Anabel & Lalwani, Anand & Xu, Yanwen & Zhang, Xiaolong & Haran, Kiruba Sivasubramaniam & Senesky, Debbie & Wang, Pingfeng, 2024. "Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. Raval, Khushi Jatinkumar & Jadav, Nilesh Kumar & Rathod, Tejal & Tanwar, Sudeep & Vimal, Vrince & Yamsani, Nagendar, 2024. "A survey on safeguarding critical infrastructures: Attacks, AI security, and future directions," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).
    15. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Xinzhou Wu & Zhe Cheng & Victor E. Kuzmichev, 2023. "Dynamic Fit Optimization and Effect Evaluation of a Female Wetsuit Based on Virtual Technology," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    18. 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).
    19. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    20. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:246:y:2024:i:c:s0951832024001157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.