Integration of an industrial control to a digital twin at the industrial level

Guerrero-Hernandez, Veronica; Reyes-Morales, Guillermo; Jacome-Onofre, Pedro; Ortega Moody, Jorge Alberto; Matacapan-Toto, Farid-Armando; Martínez Herrera, Maria Angélica

doi:10.1109/iceccme57830.2023.10253179

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32

Morales,

Escalera Canto,

Guerrero Hernandez

2023

2023 IEEE International Conference on Engineering Veracruz (ICEV)

View full text Add to dashboard Cite

Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32

Morales,

Escalera Canto,

Guerrero Hernandez

2023

2023 IEEE International Conference on Engineering Veracruz (ICEV)

View full text Add to dashboard Cite

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Hu,

Taha,

Yang

2024

Applied Sciences

View full text Add to dashboard Cite

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.

show abstract

Integration of an industrial control to a digital twin at the industrial level

Cited by 2 publications

References 30 publications

Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32

Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Contact Info

Product

Resources

About