Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique
An artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables, and the output is the failure rate of...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| التنسيق: | article |
| منشور في: |
2006
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/287/1/Paper_my_AIAA.pdf |
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| _version_ | 1864513388497862656 |
|---|---|
| author | Al-Garni, Ahmed Z. |
| author2 | Jamal, Ahmad Ahmad, Abid M. Al-Garni, Abdullah M. Tozan, Mueyyet unknown |
| author2_role | author author author author author |
| author_facet | Al-Garni, Ahmed Z. Jamal, Ahmad Ahmad, Abid M. Al-Garni, Abdullah M. Tozan, Mueyyet unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Al-Garni, Ahmed Z. Jamal, Ahmad Ahmad, Abid M. Al-Garni, Abdullah M. Tozan, Mueyyet unknown |
| dc.date.none.fl_str_mv | 2006-03-10 2020 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/287/1/Paper_my_AIAA.pdf (2006) Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique. AIAA Journal of Aircraft, 43 (2). pp. 537-543. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | AIAA |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/287/ http://www.aiaa.org/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Aerospace |
| dc.title.none.fl_str_mv | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | An artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables, and the output is the failure rate of the tires. Six years of data are used for model building and validation. Model validation, which reflects the suitability of the model for future prediction, is performed by comparing the predictions of the model with that of theWeibull regression model. The results show that the failure rate predicted by the artificial neural network more closely agrees with the actual data than the failure rate predicted by the Weibull model. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_473dd2cc432d15bf3765627a6fe9e4d2 |
| identifier_str_mv | (2006) Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique. AIAA Journal of Aircraft, 43 (2). pp. 537-543. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::287 |
| publishDate | 2006 |
| publisher.none.fl_str_mv | AIAA |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network TechniqueAl-Garni, Ahmed Z.Jamal, AhmadAhmad, Abid M.Al-Garni, Abdullah M.Tozan, MueyyetunknownAerospaceAn artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables, and the output is the failure rate of the tires. Six years of data are used for model building and validation. Model validation, which reflects the suitability of the model for future prediction, is performed by comparing the predictions of the model with that of theWeibull regression model. The results show that the failure rate predicted by the artificial neural network more closely agrees with the actual data than the failure rate predicted by the Weibull model.AIAA2006-03-102020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://eprints.kfupm.edu.sa/id/eprint/287/1/Paper_my_AIAA.pdf (2006) Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique. AIAA Journal of Aircraft, 43 (2). pp. 537-543. enhttps://eprints.kfupm.edu.sa/id/eprint/287/http://www.aiaa.org/info:eu-repo/semantics/openAccessoai::2872019-11-01T13:23:29Z |
| spellingShingle | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique Al-Garni, Ahmed Z. Aerospace |
| status_str | publishedVersion |
| title | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| title_full | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| title_fullStr | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| title_full_unstemmed | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| title_short | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| title_sort | Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique |
| topic | Aerospace |
| url | https://eprints.kfupm.edu.sa/id/eprint/287/1/Paper_my_AIAA.pdf |