- Title
- Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study
- Creator
- Siddiqui, Mustafa; Kahandawa, Gayan; Hewawasam, Hasitha
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/192871
- Identifier
- vital:18083
- Identifier
-
https://doi.org/10.1109/ICM54990.2023.10101971
- Identifier
- ISBN:9781665466615 (ISBN)
- Abstract
- Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 IEEE
- Subject
- Advance Manufacturing; Artificial Intelligence; Industrial Automation System; Industry 4.0; Predictive Maintenance. Digital Twin
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