What is Digital Twin?

Physical assets - turbines, buildings, supply chains, manufacturing lines - are expensive to experiment with. A digital twin lets engineers and operators run “what if” scenarios digitally, catching problems before they manifest in the real world.

When underpinned by a knowledge graph and a shared ontology, a digital twin can integrate data from IoT sensors, maintenance logs, design specifications, and environmental conditions into a single coherent model. This is the difference between a static 3D visualisation and a truly intelligent twin that reasons across domains.

Industries leading adoption include energy (asset integrity), manufacturing (predictive maintenance), smart cities (infrastructure management), and healthcare (patient monitoring).

A digital twin architecture typically has three layers:

1. Data ingestion: Sensor data, SCADA systems, IoT platforms, and enterprise systems feed real-time and historical data into the twin.

2. Semantic model: An ontology defines the types of assets, their properties, and their relationships. This semantic layer turns raw data points into meaningful context - a temperature reading is linked to a specific valve, on a specific pipeline, in a specific facility.

3. Analytics & simulation: With structured, contextualised data, teams can run physics-based simulations, machine learning models, and rule-based alerts. The knowledge graph enables cross-domain queries like “show me all assets in Region X that are overdue for maintenance and operating above threshold temperature”.

Offshore wind: An operator creates digital twins of each turbine, linking structural models, metocean data, and maintenance records. Predictive analytics flag early signs of bearing degradation, reducing unplanned downtime by 30%.

Smart buildings: A property manager twins an entire campus, integrating BMS data, occupancy sensors, and energy meters. The twin optimises HVAC schedules in real time, cutting energy costs by 20%.

Pharmaceutical manufacturing: A digital twin of a production line links batch records, equipment sensor data, and quality control results, enabling real-time deviation detection and faster batch release.