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https://hdl.handle.net/20.500.13091/345
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DC Field | Value | Language |
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dc.contributor.author | Çelikmıh, Kadir | - |
dc.contributor.author | İnan, Onur | - |
dc.contributor.author | Uğuz, Harun | - |
dc.date.accessioned | 2021-12-13T10:24:04Z | - |
dc.date.available | 2021-12-13T10:24:04Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1058-9244 | - |
dc.identifier.issn | 1875-919X | - |
dc.identifier.uri | https://doi.org/10.1155/2020/8616039 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/345 | - |
dc.description.abstract | There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, aK-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment. | en_US |
dc.description.sponsorship | Scientific Research Project of Havelsan and Presidency of Defence Industries project [HVL-SoZ-18/033] | en_US |
dc.description.sponsorship | This study was supported by the Scientific Research Project of Havelsan and Presidency of Defence Industries project, grant no. HVL-SoZ-18/033. | en_US |
dc.language.iso | en | en_US |
dc.publisher | HINDAWI LTD | en_US |
dc.relation.ispartof | SCIENTIFIC PROGRAMMING | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Artificial Neural-Network | en_US |
dc.subject | Support | en_US |
dc.subject | Reliability | en_US |
dc.subject | Internet | en_US |
dc.subject | System | en_US |
dc.subject | Things | en_US |
dc.title | Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1155/2020/8616039 | - |
dc.identifier.scopus | 2-s2.0-85092050243 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.authorid | Celikmih, Kadir/0000-0002-0084-6474 | - |
dc.authorwosid | Celikmih, Kadir/AAA-1357-2021 | - |
dc.identifier.volume | 2020 | en_US |
dc.identifier.wos | WOS:000570897500003 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57219278982 | - |
dc.authorscopusid | 55243323100 | - |
dc.authorscopusid | 23480734900 | - |
dc.identifier.scopusquality | Q3 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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File | Size | Format | |
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8616039.pdf | 1.46 MB | Adobe PDF | View/Open |
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