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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 fitn...

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Main Authors: Mohd Sharif, Zakaria, Mohammad Fadhil, Abas, Fatimah, Dg Jamil, Norhafidzah, Mohd Saad, Addie, Irawan, Pebrianti, Dwi
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41738/1/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles.pdf
http://umpir.ump.edu.my/id/eprint/41738/2/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles%20via%20genetic-algorithm%20methods_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41738/
https://doi.org/979-835038231-0
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spelling my.ump.umpir.417382024-07-31T03:31:14Z http://umpir.ump.edu.my/id/eprint/41738/ Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods Mohd Sharif, Zakaria Mohammad Fadhil, Abas Fatimah, Dg Jamil Norhafidzah, Mohd Saad Addie, Irawan Pebrianti, Dwi T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Institute of Electrical and Electronics Engineers Inc. 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41738/1/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles.pdf pdf en http://umpir.ump.edu.my/id/eprint/41738/2/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles%20via%20genetic-algorithm%20methods_ABS.pdf Mohd Sharif, Zakaria and Mohammad Fadhil, Abas and Fatimah, Dg Jamil and Norhafidzah, Mohd Saad and Addie, Irawan and Pebrianti, Dwi (2024) Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods. In: 2024 20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024 - Conference Proceedings. 20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024 , 1-2 March 2024 , Langkawi. pp. 75-78.. ISBN 979-835038231-0 (Published) https://doi.org/979-835038231-0
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Mohd Sharif, Zakaria
Mohammad Fadhil, Abas
Fatimah, Dg Jamil
Norhafidzah, Mohd Saad
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 Conference or Workshop Item
author Mohd Sharif, Zakaria
Mohammad Fadhil, Abas
Fatimah, Dg Jamil
Norhafidzah, Mohd Saad
Addie, Irawan
Pebrianti, Dwi
author_facet Mohd Sharif, Zakaria
Mohammad Fadhil, Abas
Fatimah, Dg Jamil
Norhafidzah, Mohd Saad
Addie, Irawan
Pebrianti, Dwi
author_sort Mohd Sharif, Zakaria
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 Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41738/1/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles.pdf
http://umpir.ump.edu.my/id/eprint/41738/2/Detecting%20problematic%20vibration%20on%20unmanned%20aerial%20vehicles%20via%20genetic-algorithm%20methods_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41738/
https://doi.org/979-835038231-0
_version_ 1822924558602076160
score 13.232414

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