TY - GEN
T1 - Machine Learning Driven Digital Twin for Industrial Control Black Box System: A Novel Framework and Case Study
AU - Siddiqui, Mustafa
AU - Kahandawa, Gayan
AU - Hewawasam, H. S
AU - Siddiqi, Muftooh Ur Rehman
PY - 2023/10/16
Y1 - 2023/10/16
N2 - 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.
AB - 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.
UR - https://ieeexplore.ieee.org/document/10275310
U2 - 10.1109/icac57885.2023.10275310
DO - 10.1109/icac57885.2023.10275310
M3 - Conference publication
BT - 2023 28th International Conference on Automation and Computing (ICAC)
PB - IEEE
ER -