Identification of rotation anomaly of fan disks using random forest model
A fan disk is one of the most crucial parts of a modern jet engine. A fan disk failure is usually very difficult to predict and has disastrous outcomes. United Airlines Flight 232 crashed due to fan disk failure, which caused hydraulic systems to be ruptured. Since then, there have been numerous att...
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2016
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| Online Access: | http://irep.iium.edu.my/54869/ http://irep.iium.edu.my/54869/1/Fault%20Monitoring%20of%20a%20turbine%20Engine%20Disk.pdf http://irep.iium.edu.my/54869/2/Programme_schedule.pdf http://irep.iium.edu.my/54869/3/List_of_publiction_with_ID_title.pdf |
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iium-548692018-05-22T01:11:24Z http://irep.iium.edu.my/54869/ Identification of rotation anomaly of fan disks using random forest model Htike@Muhammad Yusof, Zaw Zaw Nyein Naing, Wai Yan AC Collections. Series. Collected works A fan disk is one of the most crucial parts of a modern jet engine. A fan disk failure is usually very difficult to predict and has disastrous outcomes. United Airlines Flight 232 crashed due to fan disk failure, which caused hydraulic systems to be ruptured. Since then, there have been numerous attempts by researchers to come up with systems to identify anomaly rotation in fan disks. The bottleneck in non-destructive fault monitoring lies in data analysis. State-of-the-art systems are not accurate due to high dimensionality of sensory data. This paper proposes a two-layered framework to perform anomaly detection. The first layer performs dimensionality reduction using autoencoder neural networks that compress input data onto a lower dimensional manifold. The second layer classifiers whether the lower dimensional data contains any anomaly using an ensemble technique called random forest. Satisfactory preliminary results on existing datasets are quite promising and encourage us to develop a full model. 2016-07-25 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/54869/1/Fault%20Monitoring%20of%20a%20turbine%20Engine%20Disk.pdf application/pdf en http://irep.iium.edu.my/54869/2/Programme_schedule.pdf application/pdf en http://irep.iium.edu.my/54869/3/List_of_publiction_with_ID_title.pdf Htike@Muhammad Yusof, Zaw Zaw and Nyein Naing, Wai Yan (2016) Identification of rotation anomaly of fan disks using random forest model. In: International Conference on Mechanical, Automotive and Aerospace Engineering, July 25-27, 2016, Kuala Lumpur, Malaysia. (Unpublished) |
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Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English English |
| topic |
AC Collections. Series. Collected works |
| spellingShingle |
AC Collections. Series. Collected works Htike@Muhammad Yusof, Zaw Zaw Nyein Naing, Wai Yan Identification of rotation anomaly of fan disks using random forest model |
| description |
A fan disk is one of the most crucial parts of a modern jet engine. A fan disk failure is usually very difficult to predict and has disastrous outcomes. United Airlines Flight 232 crashed due to fan disk failure, which caused hydraulic systems to be ruptured. Since then, there have been numerous attempts by researchers to come up with systems to identify anomaly rotation in fan disks. The bottleneck in non-destructive fault monitoring lies in data analysis. State-of-the-art systems are not accurate due to high dimensionality of sensory data. This paper proposes a two-layered framework to perform anomaly detection. The first layer performs dimensionality reduction using autoencoder neural networks that compress input data onto a lower dimensional manifold. The second layer classifiers whether the lower dimensional data contains any anomaly using an ensemble technique called random forest. Satisfactory preliminary results on existing datasets are quite promising and encourage us to develop a full model. |
| format |
Conference or Workshop Item |
| author |
Htike@Muhammad Yusof, Zaw Zaw Nyein Naing, Wai Yan |
| author_facet |
Htike@Muhammad Yusof, Zaw Zaw Nyein Naing, Wai Yan |
| author_sort |
Htike@Muhammad Yusof, Zaw Zaw |
| title |
Identification of rotation anomaly of fan disks using random forest model |
| title_short |
Identification of rotation anomaly of fan disks using random forest model |
| title_full |
Identification of rotation anomaly of fan disks using random forest model |
| title_fullStr |
Identification of rotation anomaly of fan disks using random forest model |
| title_full_unstemmed |
Identification of rotation anomaly of fan disks using random forest model |
| title_sort |
identification of rotation anomaly of fan disks using random forest model |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/54869/ http://irep.iium.edu.my/54869/1/Fault%20Monitoring%20of%20a%20turbine%20Engine%20Disk.pdf http://irep.iium.edu.my/54869/2/Programme_schedule.pdf http://irep.iium.edu.my/54869/3/List_of_publiction_with_ID_title.pdf |
| first_indexed |
2023-09-18T21:17:34Z |
| last_indexed |
2023-09-18T21:17:34Z |
| _version_ |
1777411665156898816 |