Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network
Damage detection and structural health monitoring (SHM) of an aircraft wing exposed of changing fuel load can lead to a false alarm if the loading effects are not intelligently discriminated. This is because the loading effects can alter the vibration response and be misinterpreted as damage effects...
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| Format: | Article |
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The Aeronautical and Astronautical Society of the Republic of China
2024
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| _version_ | 1811137739475648512 |
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| author | Rahim, Sharafiz Abdul Manson, Graeme Mustapha, Faizal |
| author_facet | Rahim, Sharafiz Abdul Manson, Graeme Mustapha, Faizal |
| author_sort | Rahim, Sharafiz Abdul |
| collection | UPM |
| description | Damage detection and structural health monitoring (SHM) of an aircraft wing exposed of changing fuel load can lead to a false alarm if the loading effects are not intelligently discriminated. This is because the loading effects can alter the vibration response and be misinterpreted as damage effects. This study proposed the Principal Component Analysis (PCA)-Artificial Neural Network (ANN) for detecting damage of on aircraft wing under the effects of varying fuel tank loading conditions. A vibration test is performed on Jabiru wing which the measured signal is applied with Principal Component Analysis (PCA) to reduce the high dimensionalities and extract the features. ANN is then utilized to map the principal component indices into various damage severities and loading classes using multi-layer perceptron ANN. The results from the study show promising results when incorporating PCA with the ANN to predict various damage severities of the aircraft wing under changing fuel load conditions. |
| first_indexed | 2024-09-25T03:39:05Z |
| format | Article |
| id | upm.eprints-106222 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:39:05Z |
| publishDate | 2024 |
| publisher | The Aeronautical and Astronautical Society of the Republic of China |
| record_format | dspace |
| spelling | upm.eprints-1062222024-05-08T13:26:43Z http://psasir.upm.edu.my/id/eprint/106222/ Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network Rahim, Sharafiz Abdul Manson, Graeme Mustapha, Faizal Damage detection and structural health monitoring (SHM) of an aircraft wing exposed of changing fuel load can lead to a false alarm if the loading effects are not intelligently discriminated. This is because the loading effects can alter the vibration response and be misinterpreted as damage effects. This study proposed the Principal Component Analysis (PCA)-Artificial Neural Network (ANN) for detecting damage of on aircraft wing under the effects of varying fuel tank loading conditions. A vibration test is performed on Jabiru wing which the measured signal is applied with Principal Component Analysis (PCA) to reduce the high dimensionalities and extract the features. ANN is then utilized to map the principal component indices into various damage severities and loading classes using multi-layer perceptron ANN. The results from the study show promising results when incorporating PCA with the ANN to predict various damage severities of the aircraft wing under changing fuel load conditions. The Aeronautical and Astronautical Society of the Republic of China 2024 Article PeerReviewed Rahim, Sharafiz Abdul and Manson, Graeme and Mustapha, Faizal (2024) Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network. Journal of Aeronautics, Astronautics and Aviation, 56 (1). pp. 1-7. ISSN 1990-7710 https://www.airitilibrary.com/Article/Detail/P20140627004-N202401190010-00001 10.6125/JoAAA.202403_56(1).01 |
| spellingShingle | Rahim, Sharafiz Abdul Manson, Graeme Mustapha, Faizal Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title | Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title_full | Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title_fullStr | Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title_full_unstemmed | Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title_short | Damage detection of the Jabiru’s aircraft wing under operational fuel loading conditions using neural network |
| title_sort | damage detection of the jabiru s aircraft wing under operational fuel loading conditions using neural network |
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