Equipment RUL

Remaining Useful Life prediction for rotating equipment. A PyTorch LSTM trained on CMAPSS-style degradation data forecasts how many cycles a machine has left before failure, with a live demo you can run against your own sensor readings.

Built as the working companion to the published article on predicting equipment failure in oil & gas — the model, the preprocessing, and the demo are all open source.