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How do we engineer trustworthy digital twins?

Published online by Cambridge University Press:  05 July 2023

Peter Gorm Larsen Open the ORCID record for Peter Gorm Larsen [Opens in a new window] ,
John Fitzgerald Open the ORCID record for John Fitzgerald [Opens in a new window]  and
Jim Woodcock Open the ORCID record for Jim Woodcock [Opens in a new window]
Peter Gorm Larsen*
Affiliation:
Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
John Fitzgerald
Affiliation:
Newcastle University, Newcastle upon Tyne, UK
Jim Woodcock
Affiliation:
University of York, York, UK
*
Corresponding author: Peter Gorm Larsen; Email: [email protected]
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Extract

There has been a rapid rise of interest in the potential of digital twins to transform a vast range of Cyber-Physical System (CPS) applications, from national infrastructure to surgical robots. But what frameworks, methods and tools are needed to create and maintain digital twins on which we can depend?

Type
Question
Information
Research Directions: Cyber-Physical Systems , Volume 1 , 2023 , e3
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Context

There has been a rapid rise of interest in the potential of digital twins to transform a vast range of Cyber-Physical System (CPS) applications, from national infrastructure to surgical robots. But what frameworks, methods and tools are needed to create and maintain digital twins on which we can depend?

Digital twins are virtual replicas of real-world systems. Unlike traditional models, data and control flows couple a digital twin to the CPS of interest and must remain up to date as the CPS evolves. The twin can influence or control the CPS itself. It is not a substitute for the CPS. However, it adds value by providing analytics, visualisation or other capabilities, allowing the twin’s users to make better-informed decisions about interventions. These include preventive maintenance, response to accidental or malicious events and reconfiguration and redesign.

This digital twin vision is attractive. However, for a twin to merit the reliance we place on it, we need frameworks, methods and tools that systematically address the full range of systems’ engineering activities. We must produce evidence that adds to our confidence that a twin is fit for purpose. Digital twins for CPSs need multi-disciplinary models and analytic services, raising significant challenges: maintaining sufficient fidelity to the evolving CPS, time delays, noise in communications to and from the twin, interactions with other systems and human operators and the need for CPS elements and environments to evolve.

In this research question, we welcome contributions to the systematic engineering of trustworthy digital twins supporting CPSs to provide additional value for their users. Examples include, but are not limited to, frameworks, methods and tools for the following:

  • Systems engineering for dependable digital twins of CPSs.

  • Architectures for dependable digital twins.

  • Support for CPS operation in environments outside our control.

  • CPS anomaly detection in digital twins.

  • Addressing noise and timing delays in twin-CPS communications.

  • Composing diverse digital twins in systems-of-systems and their impact on dependability.

  • Ensuring dependability, including security and safety, of digital twins and digital twin-enabled systems.

  • Determining when to enable a CPS to be autonomous and when to rely on humans to take decisions.

  • Coping with real-time data for simulation when carrying out “what-if” scenarios.

  • Ensuring consistency between models of CPSs at different levels of abstraction inside a DT and automatically choosing the best one for the desired analysis.

We want to measure progress in answering this question. We welcome suggestions from the community for a set of public CPS case studies as benchmark problems. As a starting point, we propose a simple “hello world” case study that has proved helpful in illustrating digital twin features: an incubator. We believe the CPS community will benefit from arranging a scientific event to discuss how to establish additional and more complex curated CPS case studies to measure progress.

How to contribute to this Question

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Competing interests

The authors declare none.

References

References

Aheleroff, S, Xu, X, Zhong, RY and Lu, Y (2021) Digital Twin as a Service (DTaaS) in industry 4.0: An architecture reference model. Advanced Engineering Informatics 47, 101225.CrossRefGoogle Scholar
Barricelli, BR, Casiraghi, E and Fogli, D (2019) A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499 CrossRefGoogle Scholar
Bordeleau, F, Combemale, B, Eramo, R, van den Brand, M and Wimmer, M (2020) Towards model-driven digital twin engineering: Current opportunities and future challenges. In Babur Ö, Denil J and Vogel-Heuser B (eds.) Systems Modelling and Management. ICSMM 2020. Communications in Computer and Information Science, vol. 1262. Cham: Springer.Google Scholar
Botín-Sanabria, DM, Mihaita, A-S, Peimbert-García, RE, Ramírez-Moreno, MA, Ramírez-Mendoza, RA and Lozoya-Santos, JJ (2022) Digital twin technology challenges and applications: A comprehensive review. Remote Sensing 14, 1335.CrossRefGoogle Scholar
Fuller, A, Fan, Z, Day, C and Barlow, C (2020) Digital twin: Enabling technologies, challenges and open research. IEEE Access 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358 CrossRefGoogle Scholar
Jones, D, Snider, C, Nassehi, A, Yon, J and Hicks, B (2020) Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology 29, Part A, 36–52.CrossRefGoogle Scholar
Kritzinger, W, Karner, M, Traar, G, Henjes, J and Sihn, W (2018) Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 51, 11, 1016–1022.CrossRefGoogle Scholar
Liu, M, Fang, S, Dong, H and Xu, C (2021) Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems 58, Part B, 346–361.CrossRefGoogle Scholar
Naseri, F, Gil, S, Barbu, C, Cetkin, E, Yarimca, G, Jensen, AC, Larsen, PG and Gomes, C (2023) Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms. Renewable and Sustainable Energy Reviews 179, 113280.10.1016/j.rser.2023.113280CrossRefGoogle Scholar
Negri, E, Fumagalli, L and Macchi, M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing 11, 939–948.CrossRefGoogle Scholar
Rasheed, A, San, O and Kvamsdal, T (2020) Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143 CrossRefGoogle Scholar
Sharma, A, Kosasih, E, Zhang, J, Brintrup, A and Calinescu, A (2022) Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration 30, 100383.CrossRefGoogle Scholar
Tao, F, Zhang, H, Liu, A and Nee, AYC (2019) Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics 15, 4, 2405–2415. https://doi.org/10.1109/TII.2018.2873186 CrossRefGoogle Scholar
Tao, F, Cheng, J, Qi, Q, Zhang, M, Zhang, H and Sui, F (2018) Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94, 3563–3576.CrossRefGoogle Scholar
Tao, F, Zhang, H, Liu, A and Nee, AYC (2019) Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics 15, 4, 2405–2415. https://doi.org/10.1109/TII.2018.2873186 CrossRefGoogle Scholar
Weyns, D, Usman Iftikhar, M, de la Iglesia, DG and Ahmad, T (2012) A survey of formal methods in self-adaptive systems. In Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering - C3S2E ’12. Montreal: ACM Press, pp. 67–79. https://doi.org/10.1145/2347583.2347592 CrossRefGoogle Scholar
Feng, H, Gomes, C, Thule, C, Lausdahl, K, Sandberg, M and Larsen, PG (2021) The Incubator Case Study for Digital Twin Engineering. arXiv:2102.10309.Google Scholar
Feng, H, Gomes, C, Thule, C, Lausdahl, K, Iosifidis, A and Larsen, PG (2021) Introduction to digital twin engineering. In 2021 Annual Modeling and Simulation Conference (ANNSIM), Fairfax, VA, USA, pp. 1–12. https://doi.org/10.23919/ANNSIM52504.2021.9552135 CrossRefGoogle Scholar
Feng, H, Gomes, C, Sandberg, M, Macedo, HD and Larsen, PG (2022) Under what conditions does a digital shadow track a periodic linear physical system? In Software Engineering and Formal Methods. SEFM 2021 Collocated Workshops. SEFM 2021. Lecture Notes in Computer Science, vol. 13230. Cham: Springer. https://doi.org/10.1007/978-3-031-12429-7_11 CrossRefGoogle Scholar
Feng, H, et al. (2022) Integration of the Mape-K loop in digital twins. In 2022 Annual Modeling and Simulation Conference (ANNSIM), San Diego, CA, USA, pp. 102–113. https://doi.org/10.23919/ANNSIM55834.2022.9859489 CrossRefGoogle Scholar
Oakes, B, Parsai, A, Van Mierlo, S, Demeyer, S, Denil, J, De Meulenaere, P and Vangheluwe, H (2021) Improving digital twin experience reports. In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, pp. 179–190. ISBN 978-989-758-487-9. https://doi.org/10.5220/0010236101790190 CrossRefGoogle Scholar
Wright, T, Cláudio, G and Woodcock, J (2022) Formally verified self-adaptation of an incubator digital twin. In Leveraging Applications of Formal Methods, Verification and Validation. Practice, vol. 13704. Cham: Springer, pp. 89–109. https://doi.org/10.1007/978-3-031-19762-8_7 Google Scholar
Aheleroff, S, Xu, X, Zhong, RY and Lu, Y (2021) Digital Twin as a Service (DTaaS) in industry 4.0: An architecture reference model. Advanced Engineering Informatics 47, 101225.CrossRefGoogle Scholar
Barricelli, BR, Casiraghi, E and Fogli, D (2019) A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499 CrossRefGoogle Scholar
Bordeleau, F, Combemale, B, Eramo, R, van den Brand, M and Wimmer, M (2020) Towards model-driven digital twin engineering: Current opportunities and future challenges. In Babur Ö, Denil J and Vogel-Heuser B (eds.) Systems Modelling and Management. ICSMM 2020. Communications in Computer and Information Science, vol. 1262. Cham: Springer.Google Scholar
Botín-Sanabria, DM, Mihaita, A-S, Peimbert-García, RE, Ramírez-Moreno, MA, Ramírez-Mendoza, RA and Lozoya-Santos, JJ (2022) Digital twin technology challenges and applications: A comprehensive review. Remote Sensing 14, 1335.CrossRefGoogle Scholar
Fuller, A, Fan, Z, Day, C and Barlow, C (2020) Digital twin: Enabling technologies, challenges and open research. IEEE Access 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358 CrossRefGoogle Scholar
Jones, D, Snider, C, Nassehi, A, Yon, J and Hicks, B (2020) Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology 29, Part A, 36–52.CrossRefGoogle Scholar
Kritzinger, W, Karner, M, Traar, G, Henjes, J and Sihn, W (2018) Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 51, 11, 1016–1022.CrossRefGoogle Scholar
Liu, M, Fang, S, Dong, H and Xu, C (2021) Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems 58, Part B, 346–361.CrossRefGoogle Scholar
Naseri, F, Gil, S, Barbu, C, Cetkin, E, Yarimca, G, Jensen, AC, Larsen, PG and Gomes, C (2023) Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms. Renewable and Sustainable Energy Reviews 179, 113280.10.1016/j.rser.2023.113280CrossRefGoogle Scholar
Negri, E, Fumagalli, L and Macchi, M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing 11, 939–948.CrossRefGoogle Scholar
Rasheed, A, San, O and Kvamsdal, T (2020) Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143 CrossRefGoogle Scholar
Sharma, A, Kosasih, E, Zhang, J, Brintrup, A and Calinescu, A (2022) Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration 30, 100383.CrossRefGoogle Scholar
Tao, F, Zhang, H, Liu, A and Nee, AYC (2019) Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics 15, 4, 2405–2415. https://doi.org/10.1109/TII.2018.2873186 CrossRefGoogle Scholar
Tao, F, Cheng, J, Qi, Q, Zhang, M, Zhang, H and Sui, F (2018) Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94, 3563–3576.CrossRefGoogle Scholar
Tao, F, Zhang, H, Liu, A and Nee, AYC (2019) Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics 15, 4, 2405–2415. https://doi.org/10.1109/TII.2018.2873186 CrossRefGoogle Scholar
Weyns, D, Usman Iftikhar, M, de la Iglesia, DG and Ahmad, T (2012) A survey of formal methods in self-adaptive systems. In Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering - C3S2E ’12. Montreal: ACM Press, pp. 67–79. https://doi.org/10.1145/2347583.2347592 CrossRefGoogle Scholar
Feng, H, Gomes, C, Thule, C, Lausdahl, K, Sandberg, M and Larsen, PG (2021) The Incubator Case Study for Digital Twin Engineering. arXiv:2102.10309.Google Scholar
Feng, H, Gomes, C, Thule, C, Lausdahl, K, Iosifidis, A and Larsen, PG (2021) Introduction to digital twin engineering. In 2021 Annual Modeling and Simulation Conference (ANNSIM), Fairfax, VA, USA, pp. 1–12. https://doi.org/10.23919/ANNSIM52504.2021.9552135 CrossRefGoogle Scholar
Feng, H, Gomes, C, Sandberg, M, Macedo, HD and Larsen, PG (2022) Under what conditions does a digital shadow track a periodic linear physical system? In Software Engineering and Formal Methods. SEFM 2021 Collocated Workshops. SEFM 2021. Lecture Notes in Computer Science, vol. 13230. Cham: Springer. https://doi.org/10.1007/978-3-031-12429-7_11 CrossRefGoogle Scholar
Feng, H, et al. (2022) Integration of the Mape-K loop in digital twins. In 2022 Annual Modeling and Simulation Conference (ANNSIM), San Diego, CA, USA, pp. 102–113. https://doi.org/10.23919/ANNSIM55834.2022.9859489 CrossRefGoogle Scholar
Oakes, B, Parsai, A, Van Mierlo, S, Demeyer, S, Denil, J, De Meulenaere, P and Vangheluwe, H (2021) Improving digital twin experience reports. In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, pp. 179–190. ISBN 978-989-758-487-9. https://doi.org/10.5220/0010236101790190 CrossRefGoogle Scholar
Wright, T, Cláudio, G and Woodcock, J (2022) Formally verified self-adaptation of an incubator digital twin. In Leveraging Applications of Formal Methods, Verification and Validation. Practice, vol. 13704. Cham: Springer, pp. 89–109. https://doi.org/10.1007/978-3-031-19762-8_7 Google Scholar