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Title: | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
Authors: | AL-Khazraji, H Nasser, AR Hasan, AM Al Mhdawi, AK Al-Raweshidy, H Humaidi, AJ |
Keywords: | artificial intelligence;deep learning;remaining useful life;autoencoder;deep belief network;aircraft engine |
Issue Date: | 5-Jul-2022 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | AL-Khazraji, H., Nasser, A.R., Hasan, A.M., Al Mhdawi, A.K., Al-Raweshidy, H., Humaidi, A.J. (2022) 'Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network', IEEE, 10, pp. 82156 - 82163 (8). doi:10.1109/ACCESS.2022.3188681. |
Abstract: | © Copyright 2022 The Author(s). 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/24842 |
DOI: | https://doi.org/10.1109/ACCESS.2022.3188681 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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