Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems
Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian...
Main Authors: | Toyosi Ademujimi, Vittaldas Prabhu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-02-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/4/1430 |
Similar Items
-
Fusion-Learning of Bayesian Network Models for Fault Diagnostics
by: Toyosi Ademujimi, et al.
Published: (2021-11-01) -
A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning
by: Yan Xu, et al.
Published: (2019-01-01) -
Digital Twin Driven Fault Diagnosis Method for Subsea Control System
by: Weifeng Ge, et al.
Published: (2023-01-01) -
Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network
by: WANG Bo, et al.
Published: (2024-10-01) -
3D-AmplifAI: An Ensemble Machine Learning Approach to Digital Twin Fault Monitoring for Additive Manufacturing in Smart Factories
by: Gabriel Avelino R. Sampedro, et al.
Published: (2023-01-01)