Case study · Hydropower
Dams inspectedwithout sending anyone.
A Fortune Global 500 hydropower and water resources engineering group operating dams, substations and critical infrastructure worldwide. Digitalization in the sector is low, so safety monitoring was still largely manual, maintenance costs were rising, and there was no scientific framework for data-driven decisions across distributed assets.
Constraints and answers
The assets are remote, and the inspection was not.
Sensors on the structure, robots on the inspection
Low digital maturity
Across the water resources and hydropower industry as a whole, not only at this group.
Answer
Intelligent monitoring and sensing
Dam displacement, seepage, vibration and other structural indicators tracked in real time.
Stringent safety monitoring demands
For dams, substations and critical infrastructure, where the consequence of a missed indicator is not recoverable.
Answer
Equipment fault prediction
Operational data patterns used for predictive maintenance, before a failure rather than after it.
Rising costs
For equipment maintenance and for manual inspection, both of which scale with the number of assets.
Answer
Intelligent inspection robots
Autonomous robotic inspection in place of sending people into hazardous environments.
No multi-source decision support
There was no scientific framework for integrated operational intelligence across the estate.
Answer
Multi-source decision support platform
Sensor, operational and historical data integrated into one safety and operations intelligence layer.
Robots go where the inspector used to, and the structure reports itself in between.
The results
Inspection 300% more efficient, hazard detection up 85%.
- 300%
- INSPECTION EFFICIENCY
- In equipment inspection.
- 85%
- HAZARD DETECTION RATE
- Safety hazards found, not missed.
- 85%
- RISK PREDICTION ACCURACY
- In safety risk prediction.
- 200%
- SITE INSPECTION EFFICIENCY
- On site, as well as on equipment.
- 60%
- LABOR COST REDUCTION
- Fewer people sent into hazard.
- 40%
- LOWER INCIDENT LOSSES
- Incident-related losses down.
Six numbers, and the one the safety officer reads is the 85% detection rate.
The stack
What was deployed
Real-time monitoring of dam displacement, seepage, vibration and structural indicators through distributed sensor networks.
Pattern recognition and predictive modeling on operational data, so faults are predicted before equipment fails.
Autonomous robots doing the inspection work that used to put people in hazardous environments.
Secure infrastructure for critical water resources and hydropower operations, with data sovereignty held throughout.
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