ANN-Based Failure Modeling of T-56 Engine Turbines
The T-56 turboprop engine is one of the most widely used in military transportation aircraft. It operates virtually everywhere, from the arctic circle to the Sahara. Operation in desert conditions, however, presents a challenge for maintenance engineers regarding preventive maintenance scheduling. E...
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ftgssrrojs:oai:ojs.gssrr.org:article/14573 2023-05-15T15:07:36+02:00 ANN-Based Failure Modeling of T-56 Engine Turbines Nizar A. Qattan Ali M. Al-Bahi Belkacem Kada 2022-09-23 application/pdf https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573 eng eng International Journal of Sciences: Basic and Applied Research https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573/6410 https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573 Copyright (c) 2022 International Journal of Sciences: Basic and Applied Research (IJSBAR) https://creativecommons.org/licenses/by-nc-nd/4.0 CC-BY-NC-ND International Journal of Sciences: Basic and Applied Research (IJSBAR); Vol. 64 No. 1 (2022); 28-40 2307-4531 Reliability Neural Network Back Propagation Algorithms Turbine Blades info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2022 ftgssrrojs 2022-09-24T18:08:13Z The T-56 turboprop engine is one of the most widely used in military transportation aircraft. It operates virtually everywhere, from the arctic circle to the Sahara. Operation in desert conditions, however, presents a challenge for maintenance engineers regarding preventive maintenance scheduling. Erosion caused by sand particles drastically decreases turbine blades life. Recent studies showed that Artificial Neural Network ANN algorithms have much better capability at modeling reliability and predicting failure than conventional algorithms. In this study, more than thirty years of local operational field data were used for failure rate prediction and validation using several algorithms. These include Weibull regression modeling to establish a reference, feed-forward back-propagation ANN, and radial basis neural network algorithm. Comparison between the three methods is carried out. Results show that the failure rate predicted by both the feed-forward back-propagation artificial neural network model and radial basis neural network model are closer to actual failure data than the failure rate predicted by the Weibull model. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer. Article in Journal/Newspaper Arctic GSSRR.ORG: International Journals: Publishing Research Papers in all Fields Arctic |
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GSSRR.ORG: International Journals: Publishing Research Papers in all Fields |
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language |
English |
topic |
Reliability Neural Network Back Propagation Algorithms Turbine Blades |
spellingShingle |
Reliability Neural Network Back Propagation Algorithms Turbine Blades Nizar A. Qattan Ali M. Al-Bahi Belkacem Kada ANN-Based Failure Modeling of T-56 Engine Turbines |
topic_facet |
Reliability Neural Network Back Propagation Algorithms Turbine Blades |
description |
The T-56 turboprop engine is one of the most widely used in military transportation aircraft. It operates virtually everywhere, from the arctic circle to the Sahara. Operation in desert conditions, however, presents a challenge for maintenance engineers regarding preventive maintenance scheduling. Erosion caused by sand particles drastically decreases turbine blades life. Recent studies showed that Artificial Neural Network ANN algorithms have much better capability at modeling reliability and predicting failure than conventional algorithms. In this study, more than thirty years of local operational field data were used for failure rate prediction and validation using several algorithms. These include Weibull regression modeling to establish a reference, feed-forward back-propagation ANN, and radial basis neural network algorithm. Comparison between the three methods is carried out. Results show that the failure rate predicted by both the feed-forward back-propagation artificial neural network model and radial basis neural network model are closer to actual failure data than the failure rate predicted by the Weibull model. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer. |
format |
Article in Journal/Newspaper |
author |
Nizar A. Qattan Ali M. Al-Bahi Belkacem Kada |
author_facet |
Nizar A. Qattan Ali M. Al-Bahi Belkacem Kada |
author_sort |
Nizar A. Qattan |
title |
ANN-Based Failure Modeling of T-56 Engine Turbines |
title_short |
ANN-Based Failure Modeling of T-56 Engine Turbines |
title_full |
ANN-Based Failure Modeling of T-56 Engine Turbines |
title_fullStr |
ANN-Based Failure Modeling of T-56 Engine Turbines |
title_full_unstemmed |
ANN-Based Failure Modeling of T-56 Engine Turbines |
title_sort |
ann-based failure modeling of t-56 engine turbines |
publisher |
International Journal of Sciences: Basic and Applied Research |
publishDate |
2022 |
url |
https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
International Journal of Sciences: Basic and Applied Research (IJSBAR); Vol. 64 No. 1 (2022); 28-40 2307-4531 |
op_relation |
https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573/6410 https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14573 |
op_rights |
Copyright (c) 2022 International Journal of Sciences: Basic and Applied Research (IJSBAR) https://creativecommons.org/licenses/by-nc-nd/4.0 |
op_rightsnorm |
CC-BY-NC-ND |
_version_ |
1766339076904976384 |