Aircraft Engine Remaining Useful Life (RUL) Prediction Using Machine Learning

Knowing the Remaining Useful Life (RUL) of aircraft engines is of paramount importance in the aviation industry. RUL helps anticipate engine failures beforehand so that airlines can proactively schedule maintenance, optimize resource allocation, and reduce the risk of downtime. In this work, we consider two problems on the prediction of RUL: a binary classification problem to predict whether the engine will fail within a month, and a regression problem to predict the remaining number of operational cycles before engine failure. To this end, using the NASA C-MAPSS dataset, we trained a slew of machine learning models to address the aforementioned two problems. Our results show that the Long Short-Term Memory (LSTM) model performed the best on the binary classification problem with 0.95 precision, 0.88 recall and 0.91 F1-score on the test set, and that the Convolutional Neural Network (CNN) model [1] best on the regression problem with 14.02 RMSE on the test set. The state-of-the-art paper documented an RMSE of 16.42. Our work not only surpassed the reference RMSE but also demonstrated superior predictive accuracy. Comparing these results highlights the substantial progress achieved in predicting aircraft engine Remaining Useful Life, showcasing the effectiveness of the models developed in this study in outperforming the state-of-the-art benchmark.