Railway Asset Knowledge Graph Architecture
Short answer. A railway asset knowledge graph links routes, sections, track, structures, OHE, signalling, rolling stock, sensors, inspections, defects and work orders. It gives railway engineers and AI one connected view of the network for predictive maintenance, defect analytics and digital twins.
Architecture Guides
What problem does this solve?
Railway asset data is split across GIS, EAM, inspection systems and drawings, organised by department rather than by the network.
What does the architecture look like?
How does it work?
Linear referencing
Assets are located by route and chainage so defects, inspections and sensors can be joined spatially.
Cross-discipline links
Track, OHE and signalling assets share locations; the graph makes those dependencies queryable.
AI on the graph
Defect trends, traction energy and failure risk are computed and written back to asset nodes.
Implementation considerations
- Agree a single asset identifier scheme.
- Integrate inspection-car and sensor data incrementally.
Frequently asked questions
How does the graph relate to a railway digital twin?
The graph is the data model behind the twin; the twin adds geometry and visualisation.
Related solutions
Knowledge Graphs
A knowledge graph connects assets, locations, events, sensors, models, procedures and outcomes as explicit relationships.
SolutionDigital Twin
A digital twin is a live, queryable model of a physical asset that combines its geometry, configuration, sensor data and history.
SolutionPredictive Maintenance
AI-based predictive maintenance estimates the probability and timing of equipment failure from sensor data, operating conditions and maintenance history, so work is planned before a breakdown.
More in the Railway AI guide
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Talk it through with an engineer who builds these systems.
See Related Case Study