Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study
- Authors: Siddiqui, Mustafa , Kahandawa, Gayan , Hewawasam, Hasitha
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
- Full Text: false
- Reviewed:
- Description: 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.
Machine learning driven digital twin for industrial control black box system : a novel framework and case study
- Authors: Siddiqui, Mustafa , Kahandawa, Gayan , Hewawasam, Hasitha , Rehman Siddiqi, Muftooh
- Date: 2023
- Type: Text , Conference paper
- 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
- Full Text: false
- Reviewed:
- Description: 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.