AI for Critical Infrastructure Failure Prediction
Predict failures of bridges, pipelines, and substations using sensor data, inspections, and environmental stressors to reduce outages and hazards.
Ageing infrastructure and extreme weather raise failure risk. AI fuses structural health monitoring, inspections, and environmental data to predict failures, prioritize maintenance, and prevent catastrophic outages. Outcomes: improved safety, fewer service disruptions, and optimized capex/opex.
Third-party interference detection with geofencing/EO.
Transformer failure risk from dissolved gas analysis and load/thermal history.
Vegetation/contact risk via EO/LiDAR and weather.
Sensor/SCADA streams; inspection uploads; EO/LiDAR.
Data quality: calibration, drift checks, alignment.
Asset registry with hierarchy, age, materials, maintenance.
Frequently asked questions
What is the Implementation Roadmap?
Implementation Roadmap
What are inventory assets/data, baseline anomaly models, and CV defect detection?
Inventory assets/data; baseline anomaly models; CV defect detection.
How should risk scoring be deployed and integrated with CMMS?
The initial deployment of risk scoring should be conducted on a pilot basis, focusing on priority assets to assess its effectiveness before wider integration with the Computerized Maintenance Management System (CMMS).
How can survival/time-to-failure and climate factors be incorporated into the simulation?
To accurately model asset degradation, we are adding variables for survival time and time-to-failure, alongside incorporating climate stressors. These elements will require refinement of existing threshold values to ensure realistic simulation outcomes.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.