Predictive Maintenance Projects
Explore guided student projects in Predictive Maintenance. Build hands-on skills with real aerospace tools and data.
5 projects
Combine with other filters →Predict Engine Failure with Kaggle Data
Use real NASA sensor data to predict when a jet engine is about to fail.
Download NASA's C-MAPSS turbofan engine dataset from Kaggle and train a machine learning model to predict Remaining Useful Life (RUL)—how many cycles an engine has left before it needs maintenance. Learn how predictive maintenance saves lives and billions of dollars in aviation.
Start Project → Some AI/MLRemaining Useful Life Prediction with scikit-learn
Predict when a turbofan engine will fail before it does.
Build an end-to-end machine learning pipeline on the NASA C-MAPSS dataset to estimate turbofan engine remaining useful life (RUL). You will engineer features from raw sensor streams, train regression models, and evaluate prognostic accuracy using RMSE and scoring functions from the prognostics literature.
Start Project → AI/MLDeep Learning for Engine Prognostics
Teach an LSTM to hear an engine degrading cycle by cycle.
Build LSTM and 1D-CNN models in TensorFlow/Keras to predict turbofan engine remaining useful life from multi-sensor time-series. You will design sequence windowing pipelines, compare architectures, apply learning rate scheduling and early stopping, and interpret model predictions with attention weights.
Start Project → Some AI/MLKaggle Turbofan Competition Pipeline
Go beyond homework: build a competition-grade predictive maintenance pipeline.
Build a full competition-grade machine learning pipeline for the NASA C-MAPSS turbofan degradation challenge on Kaggle. You will implement advanced feature engineering, train a diverse model ensemble (XGBoost, LightGBM, CatBoost, neural nets), and apply stacking to push toward leaderboard-competitive RMSE — all while maintaining reproducible experiment tracking.
Start Project → AI/MLMulti-Source Predictive Maintenance Pipeline
Combine multiple aerospace datasets to build a model that generalizes across equipment types
Engineer a robust predictive maintenance system by combining multiple Kaggle aerospace and industrial datasets, training models that transfer across equipment types, and deploying a unified health monitoring pipeline. Goes beyond single-dataset ML to tackle real-world generalization challenges.
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