Abstract
Remaining Useful Life (RUL) is used to provide an early indication of failures that required
performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers
cost-effective operation for decision-makers in the industry. The availability of data using intelligence
sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a
data-driven method that has a lot of applications in the industry. One of these applications is the RUL
prediction where DL algorithms achieved good results. This paper presents an Autoencoder-based Deep
Belief Network (AE-DBN) model for Aircraft engines' RUL estimation. The AE-DBN DL model is utilized
the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The
efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and
the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square
Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior
to other DL approaches.
Original language | English |
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Pages (from-to) | 1-1 |
Number of pages | 1 |
Journal | IEEE Access |
DOIs | |
Publication status | E-pub ahead of print - 5 Jul 2022 |
Keywords
- General Engineering
- General Materials Science
- General Computer Science
- Electrical and Electronic Engineering