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 essential to accurately detect upcoming failures by predicting future behavior health state engines as well its Remaining Useful Life. use deep learning techniques estimate Life has seen a growing interest over last decade. However, hybrid methods have not been sufficiently explored yet researchers.In this paper, we proposed two-hybrid combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Long-Short Term Memory (BDLSTM), Neural Network (CNN) enhance RUL estimation. results indicate that exhibit reliable prediction accuracy significantly outperform robust predictions in literature.