RUL Estimation Enhancement using Hybrid Deep Learning Methods



Published May 3, 2021
Ikram Remadna Labib Sadek Terrissa Soheyb Ayad Nourddine Zerhouni


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

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PHM, Hybrid deep Learning, CNN, BDGRU, BDLSTM, Remaining Useful Life, RUL estimation

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