ReneWind

ReneWind is a machine learning system designed to predict equipment failures in wind turbines and support predictive maintenance. I built the model using Python, data preprocessing, and classification algorithms to identify potential failures in advance.

First, I processed historical turbine sensor data to extract meaningful operational features. Next, I trained machine learning models to classify turbine conditions and predict potential failures.

In addition, I evaluated model performance using classification metrics and optimized the pipeline to improve prediction accuracy.

As a result, the ReneWind system enables predictive maintenance strategies and helps reduce downtime in renewable energy operations.

ReneWind is an end-to-end machine learning system designed to predict wind turbine failures and support predictive maintenance in renewable energy systems. I developed the pipeline using Python and modern machine learning techniques.

First, I processed historical turbine sensor data and engineered predictive features that capture operational patterns and equipment behavior. These features help identify early indicators of potential mechanical issues.

Next, I trained classification models to detect abnormal turbine conditions and predict possible failures before they occur.

In addition, I evaluated model performance using industry-standard metrics and optimized the pipeline to improve predictive reliability.

Overall, the ReneWind system demonstrates how machine learning can transform operational sensor data into actionable insights for predictive maintenance. The project highlights my ability to design and implement AI solutions for real-world industrial and energy applications.

Stack:

Deep Learning (ANN), SMOTE, Scikit-learn, Python

  • Developed a binary classification model to predict wind turbine component failures, utilizing SMOTE techniques to resolve significant class imbalances in sensor data.
  • Optimized for Recall to minimize false negatives, successfully identifying 90% of potential failures proactively and reducing projected maintenance downtime.

“In maintenance, the cost of a false negative is often higher than a false positive — metrics must match the mission”

Randley Morales, Ph.D.Ph.D. Mathematician & Machine Learning Specialist | Generative AI, Computer Vision & Predictive Modeling

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