- Title
- Machine learning driven digital twin for industrial control black box system : a novel framework and case study
- Creator
- Siddiqui, Mustafa; Kahandawa, Gayan; Hewawasam, Hasitha; Rehman Siddiqi, Muftooh
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197207
- Identifier
- vital:18831
- Identifier
-
https://doi.org/10.1109/ICAC57885.2023.10275310
- Identifier
- ISBN:9798350335859 (ISBN)
- Abstract
- Industrial control systems are excessively used in advanced manufacturing environments. The lack of information and data regarding the internal workings of certain systems makes virtual modelling for their Digital Twin challenging. As a result, these systems are often classified as 'black box' systems. There is minimal research found on DT models for industrial control black box systems. Therefore, a novel algorithm to model the Digital Twin of the industrial control black box system in the cyber domain has been presented in this paper. Machine Learning techniques were used to develop a high-fidelity Digital Twin model of a black box system. Real-time sensor data were recorded and used to validate the proposed novel algorithm. This paper presents the proposed algorithm's effectiveness in developing a robust Digital Twin model of industrial control back box system. © 2023 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 28th International Conference on Automation and Computing, ICAC 2023, Birmingham, UK, 30 August-1 September 2023, ICAC 2023 The 28th International Conference on Automation and Computing Digitalisation for Smart Manufacturing and Systems
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2023 by the Institute of Electrical and Electronics Engineers Inc.
- Subject
- Advanced manufacturing; Digital Twin; Industrial control system; Industry 4.0; Machine learning
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