Building Digital Twins with AI and Real-Time Data Training Course

Overview of Digital Twins

• Core concepts and the historical progression of digital twins
• Applications in manufacturing, energy, and logistics sectors
• Architectural structures and lifecycle management of digital twins

System Modelling and Simulation

• Simulating dynamic systems using Simulink
• Comparing physics-based and data-driven modelling approaches
• Visualising system interactions with Unity

Real-Time Data Connectivity

• Establishing connectivity using MQTT and OPC-UA protocols
• Managing data streams with Node-RED
• Processing sensor and machine data into the digital twin

AI and Machine Learning in Digital Twins

• Embedding AI models for predictive analytics and optimisation
• Working with TensorFlow or PyTorch alongside live data
• Training models based on simulation results

Visualisation and Dashboards

• Developing user interfaces for monitoring twin performance
• Exploring 3D and 2D visualisation capabilities
• Building custom dashboards with instant insights

Case Study: Developing a Digital Twin Prototype

• Comprehensive design of a manufacturing asset twin
• Setting up data integration and machine learning components
• Deployment and testing within a simulated environment

Maintenance and Scaling of Digital Twins

• Managing the lifecycle and implementing updates
• Ensuring interoperability and adhering to standards
• Scaling solutions to encompass multiple assets or processes

Conclusion and Future Directions