RUL Estimation Enhancement using Hybrid Deep Learning Methods

The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behaviour health state of turbofan engines as well as its Remaining Useful Life (RUL). The use of Deep Learning (DL) techniques to estimate RUL has seen a growing interest over the last decade. However, hybrid DL methods have not been sufficiently explored yet by researchers. In this paper, two-hybrid methods proposed to enhance the RUL estimation by combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bidirectional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN). The results indicate that the proposed hybrid methods significantly outperform the robust predictions in the literature.