Competition Results
6 notebooks · 3 farms · 89 turbine-years · 14 models
Best CARE Score
0.319
AE Thermal · Farm C
Best Gearbox CARE
0.253
Hotelling T² · Farm C
Earliest Detection
99.7%
CUSUM Earliness · Farm C
FP Reduction
93%
Ensemble suppression · Farm B
Models
14
Across 6 notebooks
Events
95
Train + prediction sets
CARE Score Leaderboard — All Models
CARE Score Ranking — All Models & Farms
CARE Components — Best Model per Farm
Anomaly Constellation — Every Fault Event as a Star
Severity vs Earliness — All Fault Events Across All Farms — Size = Persistence · Click any star for details
| # | Farm | Notebook | Model | Coverage | Accuracy | Reliability | Earliness | CARE |
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CARE Score Race
Watch all 14 models compete as data is revealed — animated bar chart race
Models ranked by CARE Score — Bars animate to final values
Speed:
Press Play to start the race
CARE Score Distribution by Farm
Model Family Comparison — Average CARE Across Farms
Gearbox & Bearing Health
Notebooks 1a · 1b — Unsupervised detection, CUSUM, per-turbine thresholds
CUSUM Control Charts — Real Detection Results
CUSUM Control Charts — Gearbox Primary Sensor per Farm (from Notebook 1a)
Isolation Forest — Anomaly Score Distribution
IF Score Distribution — Normal vs Anomaly — Good separation = distinct peaks (from Notebook 1a)
CARE Score Components — All Farms & Models
CARE Components per Farm — Coverage × Accuracy × Reliability × Earliness (from Notebook 1a)
Per-Turbine Baseline Temperatures — Why Fleet-Wide Thresholds Fail
Baseline Temperature Range per Farm — Min to Max across turbines
CARE-Optimised vs Baseline Threshold
SAX Discord Detection — Unusual Time-Series Subsequences
SAX Discord Detection — Fault-precursor shape patterns vs Normal events
Power Curve Anomaly — Wind Speed vs Generator RPM
Power Curve: Normal vs Anomaly Events — Deviation from expected turbine performance
Severity Index — Top Gearbox Events
Severity vs Earliness — Gearbox Anomaly Events
| # | Farm | Turbine | Event | Severity | Persist (h) | Earliness | Risk |
|---|
Explainability & Maintenance Reduction
Notebook 1c — Label propagation · SHAP · XGBoost vs Gradient Boosting
Label Propagation — Coarse vs Pseudo-Labels
Pseudo-labels assign anomaly only to rows where sensors actually deviate — more precise than whole-event labels (from Notebook 1c)
SHAP Feature Importance — Global (all farms combined)
SHAP Global Feature Importance — Mean |SHAP value| across test set
SHAP Beeswarm — Feature Direction & Magnitude (global)
SHAP Beeswarm — Per Farm (XGBoost with Pseudo-Labels)
SHAP Beeswarm per Farm — Red = high value pushes toward anomaly · Blue = pushes toward normal (from Notebook 1c)
XGBoost SHAP — Top 5 Features per Farm (Interactive)
Gradient Boosting SHAP — Agreement validates sensor importance as data property
Ensemble Alert Suppression — Before vs After 30-min Window
Ensemble Voting + Alert Suppression — Raw 2/3 ensemble (top) vs after 30-min confirmation window (bottom) (from Notebook 1c)
CARE-Optimised Ensemble Results
CARE Score: Baseline vs Optimised Ensemble Threshold
XGB vs GB SHAP Agreement — Features in Top-5 of Both Models
Thermal & Electrical Signal Analysis
Notebooks 2a · 2b — Signal ranking · FFT · Correlation instability · Autoencoder
Sensor Signal Strength — Combined Score per Farm
Welch PSD — Frequency-Domain Fault Signatures
Welch PSD: Normal vs Anomaly — New spectral peaks or broadband noise increase = electrical anomaly signature (from Notebook 2a)
Correlation Instability — Thermal Sensor Decoupling
Rising instability = sensors starting to decouple = early thermal warning (from Notebook 2a)
Autoencoder Reconstruction — Actual vs Reconstructed
Growing gap = sensor behaving unlike anything in normal training data (from Notebook 2b)
Spectral & AE Summary Charts
Spectral Excess — Anomalous Frequency Content per Farm
AE Reconstruction Error — Normal vs Anomaly Mean Error
Correlation Instability — First Alert Summary
| Farm | Event | First Alert Step | Hours Into Event | Max Instability | Status |
|---|
Practical Monitoring Strategies
Notebook 2c — Ensemble · T² attribution · Operator risk dashboard
Alert Suppression — False Positive Reduction
FP / TP Before and After 30-min Confirmation Window
T² Sensor Attribution — Which Sensors to Inspect First
Interactive Threshold Simulator
Adjust Detection Threshold — See How CARE Components Change
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Coverage
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Accuracy
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Reliability
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Earliness
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CARE Score
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CARE-Optimised Thresholds
| Farm | Baseline CARE | Optimised CARE | Improvement | Opt. Threshold |
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Operator Risk Dashboard — All Anomaly Events Ranked
Severity vs Earliness — Thermal Anomaly Events
| # | Farm | Turbine | Event | Severity | Persist (h) | Earliness | Risk |
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Model Duel
Pick any two models and a farm — head-to-head CARE component breakdown
Select Two Models to Compare
Head-to-Head CARE Components Radar
Maintenance Cost Calculator
Estimate real-world savings from your model results vs naive always-alert baseline