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DC Field | Value | Language |
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dc.contributor.author | AL-Khazraji, H | - |
dc.contributor.author | Nasser, AR | - |
dc.contributor.author | Hasan, AM | - |
dc.contributor.author | Al Mhdawi, AK | - |
dc.contributor.author | Al-Raweshidy, H | - |
dc.contributor.author | Humaidi, AJ | - |
dc.date.accessioned | 2022-07-11T14:42:41Z | - |
dc.date.available | 2022-07-11T14:42:41Z | - |
dc.date.issued | 2022-07-05 | - |
dc.identifier.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. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24842 | - |
dc.description.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. | en_US |
dc.format.extent | 82156 - 82163 (8) | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | © Copyright 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | artificial intelligence | en_US |
dc.subject | deep learning | en_US |
dc.subject | remaining useful life | en_US |
dc.subject | autoencoder | en_US |
dc.subject | deep belief network | en_US |
dc.subject | aircraft engine | en_US |
dc.title | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/ACCESS.2022.3188681 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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