Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method

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dc.contributor.authorCelikmih, Kadir
dc.contributor.authorInan, Onur
dc.contributor.authorUguz, Harun
dc.date.accessioned2024-02-23T14:26:38Z
dc.date.available2024-02-23T14:26:38Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractThere 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.sponsorshipScientific Research Project of Havelsan and Presidency of Defence Industries project [HVL-SoZ-18/033]en_US
dc.description.sponsorshipThis 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.identifier.doi10.1155/2020/8616039
dc.identifier.issn1058-9244
dc.identifier.issn1875-919X
dc.identifier.scopus2-s2.0-85092050243en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1155/2020/8616039
dc.identifier.urihttps://hdl.handle.net/20.500.12452/14251
dc.identifier.volume2020en_US
dc.identifier.wosWOS:000570897500003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofScientific Programmingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[Keyword Not Available]en_US
dc.titleFailure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Methoden_US
dc.typeArticleen_US

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