Industrial Informatics and Digital Twin

Dear Colleagues,

The essence of the digital twin is virtual–real fusion, which uses sensors, computational models, and industrial data to provide predictions for the current and future states of physical systems. The expected benefit of adopting this high-fidelity approach is to ultimately reduce the uncertainty in the performance of the physical automated system in use.

To capture and comprehend the full complexity of automated systems, the fusion of multi-physics, multi-scale, multi-stage industrial data is required in the digital twin, and frequent and regular (even real-time) updates of the previous predictions through the acquired data are also essential. However, huge challenges still lie in handling the high data variety, complexity, and timeliness embedded in the controlling and decision-making in automated systems.

Industrial information technology, such as AR/VR, cloud computing, deep learning, and knowledge graph, is changing dramatically and has shown promising prospects for managing massive industrial data, establishing digital twin models, and enabling smart services for automated systems. To this end, this Special Issue solicits articles relating to the automation area for digital twins, and concentrates on digital twin models, digital twin informatics, and digital twin behavior in automatic systems to develop research on self-decision making, self-adaptation, and automatic evolution of automatic systems. Topics of interest include but are not limited to the following areas:

1. Methodologies for digital twins of automation system

System architectures for digital twins;

Representation and modelling for digital twins.

2. Industrial informatics for digital twins

Industrial knowledge graph of digital twins;

Graph neural networks for industrial knowledge graph;

Verification techniques for digital twins.

3. Digital Twin-Driven Approaches for automation system

Integrating digital twins with existing industrial approaches such as Industry 4.0;

Augmented reality and virtual reality;

Informatics-based and digital twin-enabled industrial services.

Prof. Dr. Dan Zhang
Prof. Dr. Jinsong Bao
Guest Editors

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