with Turbit AI
Resolve unnoticed underperformance or work on abnormal behavior patterns from specific components. Optimize detection performance with relvent feedback.
Deploy Turbit AI Monitoring
Technical Set Up
For each turbine, component and each site, Turbit AI learns the normal performance behavior from historical SCADA data. Turbit models use physically relevant input data such as wind speed, temperature, wind direction and turbulence intensity.
Continuous simulation of the normal behavior (measured values). Turbit AI detects deviations from the dynamic normal behavior and provides this anomaly as an automatic report. Each report is accessible via link or API.
Evaluate every anomaly detection. This feedback continuously improves the Turbit AI:
Communication per wind turbine
Precise anomaly detection
Prediction of root causes
Evolving from Monitoring
to an AI operating system
Understanding every data point
Turbit constantly enriches SCADA data with a wide range of simulated data to better understand complex behavior patterns of each wind turbine. Turbit AI works with every wind turbine regardless of the manufacturer and site.
Deep neural networks use enriched SCADA data for anomaly detection and root cause prediction. Understanding patterns of the anomaly like linear trends or a sharp temperature increase is crucial for understanding the issue at hand and for determining urgency.
Feedback from technical operators constantly improves the failure database and the (root cause) detection performance. In Addition, feedback helps to increase communication relevance from Turbit AI on a wind turbine and park level.
Optimized (root cause) detection performance and communication relevance from Turbit AI are the technical prerequisites for scaling an AI operating system for wind turbines. Implementing new processes and roles are equally important. Talk to a Turbit Expert to better understand organizational adjustment while scaling.