Structural fault diagnosis of UAV based on convolutional neural network and data processing technology
This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was develo...
| Main Authors: | , , , , |
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| Format: | Article |
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Informa UK Limited
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/110495/ |
| _version_ | 1848865530867351552 |
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| author | Ma, Yumeng Mustapha, Faizal Ishak, Mohamad Ridzwan Abdul Rahim, Sharafiz Mustapha, Mazli |
| author_facet | Ma, Yumeng Mustapha, Faizal Ishak, Mohamad Ridzwan Abdul Rahim, Sharafiz Mustapha, Mazli |
| author_sort | Ma, Yumeng |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. |
| first_indexed | 2025-11-15T14:06:11Z |
| format | Article |
| id | upm-110495 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:06:11Z |
| publishDate | 2023 |
| publisher | Informa UK Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1104952025-07-22T02:07:34Z http://psasir.upm.edu.my/id/eprint/110495/ Structural fault diagnosis of UAV based on convolutional neural network and data processing technology Ma, Yumeng Mustapha, Faizal Ishak, Mohamad Ridzwan Abdul Rahim, Sharafiz Mustapha, Mazli This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. Informa UK Limited 2023 Article PeerReviewed Ma, Yumeng and Mustapha, Faizal and Ishak, Mohamad Ridzwan and Abdul Rahim, Sharafiz and Mustapha, Mazli (2023) Structural fault diagnosis of UAV based on convolutional neural network and data processing technology. Nondestructive Testing and Evaluation, 39 (2). pp. 426-445. ISSN 1058-9759; eISSN: 1477-2671 https://www.tandfonline.com/doi/full/10.1080/10589759.2023.2206655 10.1080/10589759.2023.2206655 |
| spellingShingle | Ma, Yumeng Mustapha, Faizal Ishak, Mohamad Ridzwan Abdul Rahim, Sharafiz Mustapha, Mazli Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title | Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title_full | Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title_fullStr | Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title_full_unstemmed | Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title_short | Structural fault diagnosis of UAV based on convolutional neural network and data processing technology |
| title_sort | structural fault diagnosis of uav based on convolutional neural network and data processing technology |
| url | http://psasir.upm.edu.my/id/eprint/110495/ http://psasir.upm.edu.my/id/eprint/110495/ http://psasir.upm.edu.my/id/eprint/110495/ |