Somers, R.J., Clark, A.G., Walkinshaw, N. et al. (1 more author) (2022) Reliable counterparts : efficiently testing causal relationships in digital twins. In: MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, 23-28 Oct 2022, Montreal, Quebec, Canada. Association for Computing Machinery , pp. 468-472. ISBN 9781450394673
Abstract
The lack of testability of digital twins poses several difficulties when developing reliable systems. Intricate models complicate the definition of comprehensive testing criteria, and physical couplings make obtaining test data an arduous task. To alleviate these challenges, we explore the use of causal inference based testing and propose a technique to allow for correct behaviour of digital twins to be captured in causal diagrams, which are then tested with an efficient data set through the use of counterfactuals. We explore a motivating example of a robotic arm to show how this technique can confirm known causal relationships in a system, and even uncover a fault in the system which caused dangerous behaviour. Our technique localised this erroneous behaviour to a single causal relationship between two variables. Having shown this technique works with a case study, we explore its limitations and the challenges when approaching other industrial applications.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | digital twin; causal inference; testing; fault localisation; cyber-physical system |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T030526/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Sep 2022 11:02 |
Last Modified: | 15 Dec 2022 15:55 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3550356.3561589 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190678 |