An encoder-transformer architecture-based framework for multi-variate time series prediction with a prognostics use case.
Advanced Condition Monitoring and Remaining Useful Life Prediction Framework using Deep Learning for Industrial Equipment Prognosis and Predictive Maintenance
End-to-end predictive maintenance system combining adaptive health monitoring, anomaly detection, probabilistic RUL prediction, failure risk estimation, drift detection, and reliability-aware maintenance decisions.
Machine Learning project to predict the Remaining Useful Life (RUL) of turbofan engines using the NASA CMAPSS dataset and Random Forest Regression.
Industrial asset health monitoring — know which machines need attention before they fail. Multi-source sensor ingestion, anomaly detection, and fault diagnosis. Validated on NASA C-MAPSS.
End-to-end predictive maintenance pipeline for turbofan engine Remaining Useful Life (RUL) prediction using the NASA CMAPSS dataset.
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