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This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.
Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
Predict remaining useful life of a component based on historical sensor observations using automated feature engineering
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
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