Time series analysis for predictive maintenance of turbofan engines

These effects are often shown using the test set, something which is considered (very) bad practice but helps for educational purposes. Welcome to another installment of the'Exploring NASA's turbofan dataset' series. This will be the third analysis on FD001, where all engines run on the same operating condition and develop the same fault. Initially we assumed the Remaining Useful Life (RUL) of the engines to decline linearly. Clipping the RUL improved the baseline linear regression by 31% (from an RMSE of 31.95 to an RMSE of 21.90). We then switched to a Support Vector Regression and squeezed out another 6% improvement for a total RMSE of 20.54.