An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems

An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems

Abstract In recent times, digital twins (DT) is becoming an emerging and key technology for smart industrial control systems and Industrial Internet of things (IIoT) applications. The DT presently supports a significant tool that can generate a huge dataset for fault prediction and diagnosis in a re...

Full description

Bibliographic Details
Main Authors: Samar M. Zayed, Gamal Attiya, Ayman El-Sayed, Amged Sayed, Ezz El-Din Hemdan
Format: Article
Language:English
Published: Springer 2023-05-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Digital twins (DT)
Flower pollination algorithm (FPA)
Optimization
Machine learning
Fault diagnosis
Control systems
Online Access:https://doi.org/10.1007/s44196-023-00241-6

Internet

https://doi.org/10.1007/s44196-023-00241-6

Similar Items