27.-28. November 2025: Finance Meets Wind Forum - Jetzt anmelden!
BLADE
MONITORING
Reduce blade failure risk with unmatched precision. Turbit Blade Monitoring combines advanced AI analytics with high-frequency sensor data to detect early-stage blade damage in real time. Get notified when it matters to prevent cascading damage, minimize downtime, and extend turbine life.
PROTECT BLADE HEALTH AND PERFORMANCE WITH AI.
GAIN EARLY DETECTION OF DAMAGE.
PLAN INTERVENTIONS WITH CONFIDENCE.
EXTEND TURBINE LIFETIME AND REDUCE COSTS.
Analyze flap and edge vibrations continuously to identify cracks, delamination, or abnormal loads before they escalate.
Alerts provide clear context on blade condition and failure risk, supporting precise maintenance decisions and efficient resource allocation.
By acting before small defects turn into major failures, you can prevent cascading damage, minimize downtime, and extend the overall lifetime of turbine blades.
LESS TOOL CLUTTER
Combines Blade and SCADA Monitoring in one platform.
CUTTING-EDGE AI
Real-time spectrogram analysis with AI brings deeper context for faster, more accurate failure analysis.
TURBIT BLUE INTEGRATION
Enables holistic risk coverage and potential OPEX reduction.
OUR SENSOR PARTNER.
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Turbit Blade Monitoring works seamlessly with high-frequency data from Weidmüller’s BladeControl.
Also compatible with other blade sensor systems via Turbit Datahub, so operators can integrate different setups across their fleets.
TURBIT AI INFRASTRUCTURE.
The four steps of Turbit’s fully automated and self-improving AI infrastructure.
1. Data Engineering
Raw Data Collection
Data Lake and Warehouse
Data Streaming
2. Anomaly Detection
Training Data Set
Deep NN* predict normal behaviour
Outlier Detection
3. Classification
Anomaly Classification
Failure Mode Prediction
Relevance Prediction
4. Feedback
Customer Feedback Functionalities
Communication
with Service
Reporting & Analytics Tools
Retraining NN
Failure Database
*NN: Neural Networks
