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WORKING 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.

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HOW TURBIT WORKS

Difference

GENERAL ALARM SETTINGS

In general, Turbit Detection KPIs measure the difference between simulated data from neural networks and actual data from the wind turbine. When this difference reaches a certain threshold an alarm will be created.   

  

The thresholds are set dynamically from Turbit depending on the neural network (NN) performance and prediction certainty per analytics module per turbine.  

 

 

 
Data Mapping

INCREASE MODEL PERFORMANCE

To improve NN performance and prediction certainty, we manage training data sets per neural network while onboarding and monitoring the wind turbine.    

With every confirmed alarm, Turbit automatically adds and excludes data for the training data set. Our retraining schedules ensure regular retraining to bring better-performing neural networks into production.  

 
 
Module

ALARM SETTINGS FOR TURBIT MODULES

On a high-level Turbit uses different Detection KPIs for AI Monitoring Modules. While Detection KPIs for Power Monitoring analyze shorter periods (last 30 Minutes). Detection KPIs for the Main Components look at a longer time frames (5-10 days) to ensure high alarm relevance.  

  

For power monitoring and wildlife monitoring, we add detailed filters based on status codes and additional information from the operation software like Bazefield to ensure high alarm relevance. We can set filters per turbine and module. 

 
 
Alerts

AUTOMATIC REPORTS

Each automatic alert comes with an automatically generated report. The report is built up to interpret alerts quickly. The report is called Event Card. The Event Card displays relevant plots, benchmarks of nearby turbines, and overlapping status codes of the anomaly. 

 
 

SMART DASHBOARD AND ALERTS

Overview of data quality, model performance, and anomalies sent per Turbit  Module booked. The Dashboard and the Case Card are the central design pattern of the Turbit Platform.  

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DASHBOARD

  • Data quality

  • Smart Alerts

  • KPIs

  • Model performance

 

CASE CARD

  • Plots 

  • Benchmarks

  • Label prediction

  • Statistics

 
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TURBIT AI INFRASTRUCTURE

TURBIT AI INFRASTRUCTURE

 

The four steps of Turbit’s fully automated and self-improving AI infrastructure. 

1. DATA ENGINEERING
2. ANOMALY DETECTION

Raw Data Collection

Data Lake and Warehouse

Data Streaming

Training Data Set

Deep NN* predict normal behaviour

Outlier Detection

3. CLASSIFICATION
4. FEEDBACK

Anomaly Classification

Failure Mode Prediction

Relevance Prediction

Customer Feedback Functionalities

Communication 

with Service

Reporting & Analytics Tools

   Failure Database   

*NN: Neutral Networks

   Retraining NN   

DEPLOY TURBIT MONITORING

Settings

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.

AI Monitoring

AI MONITORING

 

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.

Transfer

FEEDBACK LOOP

Evaluate every anomaly detection. This feedback continuously improves the Turbit AI: 

 

  • Communication per wind turbine

  • Precise anomaly detection

  • Root cause predtiction

WORKFLOWS WITH TURBIT

TURBIT

  • Providing in-depths root cause analysis

   

  • Preparing video analysis for better communications

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  • Dashboards for fleet health, data quality and model performance

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TURBIT & CUSTOMER

  • Verify root causes

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  • Prepare preventive measure proposals for OEM or service

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  • Reports and KPIs for internal and external communication   

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CUSTOMER

  • Act on early diagnosis of potential component damage

   

  • Communicates with service partners and Turbit

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  • Provides direct feedback to Turbit after maintenance​​          ​

HOW TO START

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

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