with Turbit AI

Detect, investigate and remedy underperformance at an early stage. Abnormal behavior patterns of components are detected immediately. Through feedback from our customers, Turbit's AI is constantly improving.


Deploy Turbit AI Monitoring


Technical Set Up

AI Monitoring

Feedback Loop

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


Event Card

  • Plots 

  • Benchmarks

  • Label prediction

  • Statistics



  • Data quality

  • Smart Alerts

  • KPIs

  • Model performance


Workflows with Turbit



  • Providing in-depths root cause analysis


  • Preparing video analysis for better communications

  • Dashboards for fleet health, data quality and model performance


Turbit & Customer

  • Verify root causes

  • Prepare preventive measure proposals for OEM or service

  • Reports and KPIs for internal and external communication



  • Act on early diagnosis of potential component damage


  • Communicates with service partners and Turbit

  • Provides direct feedback to Turbit after maintenance

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.

Issue Classification

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.

How to Start

Connect your operations software stack to Turbit or upload SCADA data.

Book a demo and learn how Turbit AI changes operation and maintenance for wind turbines.