Predict Equipment Failure for a Wind-Farm Operator
Overview
What this challenge is about.
You engineer features and compare three models to predict turbine gearbox failures, then deliver a complete analysis. You get a verifiable certificate.
The scenario
The operator (around 180 staff, EUR 220M annual revenue) loses about EUR 1.2M per year to unplanned gearbox replacements that could have been scheduled.
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.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Regularized Regression
Apply regularized regression to solve real industry problems and demonstrate production-level capability.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
- Kernel Methods
Apply kernel methods to solve real industry problems and demonstrate production-level capability.
- Calibration
Apply calibration to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
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