Comparative Analysis of Deep Learning and Decision Tree Approaches for Predicting Aircraft Engine Remaining Useful Life

Abstract

Accurate prediction of Remaining Useful Life (RUL) is crucial for Prognostics and Health Management (PHM), particularly in predictive maintenance strategies aimed at ensuring the reliability of industrial systems. This study compares two approaches for RUL prediction of aircraft engines: a deep learning-based one-dimensional Convolutional Neural Network (CNN-1D) and a traditional Decision Tree (DT) algorithm, using data from the C-MAPSS dataset. The results show that the CNN-1D model significantly outperforms the DT model, achieving a Root Mean Square Error (RMSE) of 21.44 on the training set and 27.12 on the test set, compared to the DT model’s RMSE of 23.83 and 28.93, respectively. These findings highlight the superior capability of deep learning techniques in RUL estimation, underscoring their importance in PHM and predictive maintenance applications.

Keywords

• remaining useful life
• deep learning
• convolution neural networks
• predictive maintenance
• prognostics and health management