Overview
ReneWind is a predictive maintenance solution for wind turbine generators. Using neural network classification models, the system predicts potential generator failures before they occur, enabling proactive maintenance strategies that reduce downtime and optimize operational efficiency in renewable energy infrastructure.
Business Problem
Wind turbine generator failures are costly — both in terms of repair expenses and lost energy production during downtime. Traditional reactive maintenance means turbines operate until failure, leading to expensive emergency repairs and extended outages.
Approach
- Data: Sensor data from wind turbine generators (temperature, vibration, pressure, power output).
- Model: Neural network classification to predict failure/no-failure outcomes.
- Features: Time-series sensor readings aggregated into predictive features.
- Strategy: Proactive maintenance scheduling based on predicted failure probability.
Key Techniques
- Neural network architecture for binary classification
- Feature engineering from time-series sensor data
- Model evaluation with focus on recall
- Business strategy for maintenance scheduling optimization
Tech Stack
Neural Networks Classification