Predict Equipment Failure for a Wind-Farm Operator
Overview
What this challenge is about.
You receive 18 months of SCADA (Supervisory Control and Data Acquisition — the standard turbine telemetry feed) data sampled every 10 minutes from all 240 turbines, with labeled failure events (about 60 confirmed gearbox failures). Engineer features at 1-hour, 6-hour, and 24-hour windows. Fit logistic regression with L1/L2 penalties, XGBoost, and a kernel SVM. Compare on a temporally-split test set with precision at recall = 0.6. Quantify calibration. Deliver the modeling notebook, methodology memo, and a 2-page operations brief.
The Brief
What you'll do, and what you'll demonstrate.
Predict gearbox failure 7-14 days ahead at high precision using classical statistical learning, and document the methodology.
Earning criteria — what you'll demonstrate
- Apply regularized regression, boosting, and kernel SVMs to a real classification problem
- Engineer temporal features without leakage
- Compare models with operationally-meaningful metrics
- Quantify calibration and recommend a threshold
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Data Scientist
Classical statistical-learning modeling with rigorous temporal evaluation and an ops-facing memo is the textbook data-scientist project at any industrial-IoT company.
This challenge sharpens
- classification
- feature-engineering
- calibration
Machine Learning Engineer
Choosing among regularized regression, boosting, and SVMs by operational metrics is the same trade-off MLEs make in production-model selection.
This challenge sharpens
- regularized-regression
- gradient-boosting
- kernel-methods
Applied AI Scientist
Translating model output into a threshold recommendation that ops can use is core applied-AI scientist work.
This challenge sharpens
- calibration
- feature-engineering
- classification