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Detection Of Brushes In Hydroelectric Water
A global renewables company leveraged computer vision and AI models to detect and measure brush accumulation in dam waters, enabling real-time monitoring, early intervention, and improved operational efficiency across hydroelectric assets
Early detection
Real-time brush monitoring
Operational efficiency
Reduced manual inspections
Model scalability
Reusable across locations
Resource optimization
Targeted maintenance actions
AI-driven detection enables proactive dam management
Ensuring efficient and proactive dam maintenance
Manual monitoring and lack of real-time insights limited the ability to detect and manage brush accumulation before impacting operations
Key Challenges
Lack of real-time visibility of brush accumulation
Detection depended on manual inspections without continuous monitoring
Detection depended on manual inspections without continuous monitoring
Risk of flow obstruction and efficiency loss
Undetected brush buildup could impact water flow and generation performance
Undetected brush buildup could impact water flow and generation performance
Inefficient use of maintenance resources
Interventions were not always timely or based on actual need
Interventions were not always timely or based on actual need
Complexity in scaling detection across locations
Different camera perspectives and environments required adaptable models
Different camera perspectives and environments required adaptable models
Solution
AI-based computer vision for real-time brush detection
Real-time brush detection through AI models
Developed algorithms to identify and measure brush accumulation
Early warning system for proactive intervention
Enabled alerts when brush levels reached critical thresholds
Data-driven maintenance optimization
Provided actionable insights to trigger maintenance only when needed
Scalable model adaptable to multiple cameras
Implemented a single Nested-Unet model adaptable to different locations
Impact
Enhancing efficiency through predictive monitoring
The solution enabled early detection of obstructions, improved operational efficiency, and optimized maintenance efforts by shifting from reactive to data-driven decision-making
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