Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network
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 se...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9815246/ |
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| author | Huthaifa Al-Khazraji Ahmed R. Nasser Ahmed M. Hasan Ammar K. Al Mhdawi Hamed Al-Raweshidy Amjad J. Humaidi |
| author_facet | Huthaifa Al-Khazraji Ahmed R. Nasser Ahmed M. Hasan Ammar K. Al Mhdawi Hamed Al-Raweshidy Amjad J. Humaidi |
| author_sort | Huthaifa Al-Khazraji |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:40:30Z |
| format | Article |
| id | doaj.art-4d7d7752681248dda6e60df1d905f9de |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:40:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d7d7752681248dda6e60df1d905f9de2022-12-22T03:59:00ZengIEEEIEEE Access2169-35362022-01-0110821568216310.1109/ACCESS.2022.31886819815246Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief NetworkHuthaifa Al-Khazraji0https://orcid.org/0000-0002-6290-3382Ahmed R. Nasser1Ahmed M. Hasan2Ammar K. Al Mhdawi3https://orcid.org/0000-0003-1806-1189Hamed Al-Raweshidy4https://orcid.org/0000-0002-3702-8192Amjad J. Humaidi5Control and Systems Engineering Department, University of Technology, Baghdad, IraqControl and Systems Engineering Department, University of Technology, Baghdad, IraqControl and Systems Engineering Department, University of Technology, Baghdad, IraqDepartment of Computer Science, Edge Hill University, Ormskirk, U.K.Department of Electrical and Electronic Engineering, Brunel University London, Uxbridge, U.K.Control and Systems Engineering Department, University of Technology, Baghdad, IraqRemaining 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.https://ieeexplore.ieee.org/document/9815246/Artificial intelligencedeep learningremaining useful lifeAutoencoderdeep belief networkaircraft engine |
| spellingShingle | Huthaifa Al-Khazraji Ahmed R. Nasser Ahmed M. Hasan Ammar K. Al Mhdawi Hamed Al-Raweshidy Amjad J. Humaidi Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network IEEE Access Artificial intelligence deep learning remaining useful life Autoencoder deep belief network aircraft engine |
| title | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
| title_full | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
| title_fullStr | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
| title_full_unstemmed | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
| title_short | Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network |
| title_sort | aircraft engines remaining useful life prediction based on a hybrid model of autoencoder and deep belief network |
| topic | Artificial intelligence deep learning remaining useful life Autoencoder deep belief network aircraft engine |
| url | https://ieeexplore.ieee.org/document/9815246/ |
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