Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods
Unmanned Aerial Vehicles (UAV) problematic vibration detection as a flaw detection and identification (FDI) method has emerged as a feasible tool for assessing a UAV’s health and condition. This paper shows the potential of optimization-based UAV problematic vibration detection. A proposed fitness f...
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IEEE
2024
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| Online Access: | http://irep.iium.edu.my/112276/1/112276_Detecting%20problematic%20vibration.pdf http://irep.iium.edu.my/112276/2/112276_Detecting%20problematic%20vibration_SCOPUS.pdf http://irep.iium.edu.my/112276/ https://ieeexplore.ieee.org/document/10525348 |
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my.iium.irep.1122762024-06-04T00:56:40Z http://irep.iium.edu.my/112276/ Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods Zakaria, Mohd Sharif Abas, Mohammad Fadhil Dg Jamil, Fatimah Mohd Saad, Norhafidzah Hashim, Addie Irawan Pebrianti, Dwi T Technology (General) TJ Mechanical engineering and machinery TJ212 Control engineering TL Motor vehicles. Aeronautics. Astronautics TL500 Aeronautics Unmanned Aerial Vehicles (UAV) problematic vibration detection as a flaw detection and identification (FDI) method has emerged as a feasible tool for assessing a UAV’s health and condition. This paper shows the potential of optimization-based UAV problematic vibration detection. A proposed fitness function based on the frequency domain has been detailed. The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. The test results show promising results with obtained mean RMSE =1407.2303, mean MAPE =0.7135, and mean detection time =2.6129s for a data range of between 3955 to 9057. IEEE 2024-05-14 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/112276/1/112276_Detecting%20problematic%20vibration.pdf application/pdf en http://irep.iium.edu.my/112276/2/112276_Detecting%20problematic%20vibration_SCOPUS.pdf Zakaria, Mohd Sharif and Abas, Mohammad Fadhil and Dg Jamil, Fatimah and Mohd Saad, Norhafidzah and Hashim, Addie Irawan and Pebrianti, Dwi (2024) Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods. In: 20th International Colloquium on Signal Processing & Its Applications (CSPA 2024), 1st - 2nd March 2024, Langkawi, Malaysia. https://ieeexplore.ieee.org/document/10525348 10.1109/CSPA60979.2024.10525348 |
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English English |
| topic |
T Technology (General) TJ Mechanical engineering and machinery TJ212 Control engineering TL Motor vehicles. Aeronautics. Astronautics TL500 Aeronautics |
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T Technology (General) TJ Mechanical engineering and machinery TJ212 Control engineering TL Motor vehicles. Aeronautics. Astronautics TL500 Aeronautics Zakaria, Mohd Sharif Abas, Mohammad Fadhil Dg Jamil, Fatimah Mohd Saad, Norhafidzah Hashim, Addie Irawan Pebrianti, Dwi Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| description |
Unmanned Aerial Vehicles (UAV) problematic vibration detection as a flaw detection and identification (FDI) method has emerged as a feasible tool for assessing a UAV’s health and condition. This paper shows the potential of optimization-based UAV problematic vibration detection. A proposed fitness function based on the frequency domain has been detailed. The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. The test results show promising results with obtained mean RMSE =1407.2303, mean MAPE =0.7135, and mean detection time =2.6129s for a data range of between 3955 to 9057. |
| format |
Proceeding Paper |
| author |
Zakaria, Mohd Sharif Abas, Mohammad Fadhil Dg Jamil, Fatimah Mohd Saad, Norhafidzah Hashim, Addie Irawan Pebrianti, Dwi |
| author_facet |
Zakaria, Mohd Sharif Abas, Mohammad Fadhil Dg Jamil, Fatimah Mohd Saad, Norhafidzah Hashim, Addie Irawan Pebrianti, Dwi |
| author_sort |
Zakaria, Mohd Sharif |
| title |
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| title_short |
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| title_full |
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| title_fullStr |
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| title_full_unstemmed |
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| title_sort |
detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods |
| publisher |
IEEE |
| publishDate |
2024 |
| url |
http://irep.iium.edu.my/112276/1/112276_Detecting%20problematic%20vibration.pdf http://irep.iium.edu.my/112276/2/112276_Detecting%20problematic%20vibration_SCOPUS.pdf http://irep.iium.edu.my/112276/ https://ieeexplore.ieee.org/document/10525348 |
| _version_ |
1800897056885178368 |
| score |
13.211869 |