Damage identification for multi-rotor drone using convolutional neural network technique

In contemporary society, multi-rotor drone have found extensive usage in various fields, such as agriculture, cargo transportation, and aerial photography. Damage to multi-rotor drone can compromise their safety and reduce efficiency. Therefore, early damage detection is crucial as it can prevent...

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Main Author:	Ma, Yumeng
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Flying-machines. Rotors. Neural networks (Computer science).
Online Access:	http://psasir.upm.edu.my/id/eprint/111808/1/FK%202023%203%20IR.pdf
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id	my-upm-ir.111808
record_format	uketd_dc
spelling	my-upm-ir.1118082024-08-27T07:48:41Z Damage identification for multi-rotor drone using convolutional neural network technique 2023-09 Ma, Yumeng In contemporary society, multi-rotor drone have found extensive usage in various fields, such as agriculture, cargo transportation, and aerial photography. Damage to multi-rotor drone can compromise their safety and reduce efficiency. Therefore, early damage detection is crucial as it can prevent catastrophic failures and decrease the associated economic and human costs. At present, visual inspection is the primary method used for detecting damage in multi- rotor drone. However, this technique may not be entirely reliable in identifying minor faults that are difficult to discern with the naked eye. This study focuses on three experimental parts; firstly,to fabricate a multi-rotor drone as the research subject; secondly, to develop a vibration data acquisition device with MPU6050 and STM32 micro-controller,and thirdly,to identify the damage using machine learning techniques. Damage scenarios were set by releasing the bolts at different conjunction points of the multi-rotor body frame.Three damaged cases were set by releasing one bolt at arm conjunction, two bolts at arm conjunction and one bolt at motor conjunction, respectively. The first case (undamaged) is considered as the reference. Any change in structure can reflect in a vibration signal. Three axes vibration data were acquired under different conditions,for the sake of safety,the UAV was conducted under the ground with a idol motor speed. After the data collection,the data preprocessing techniques linear interpolation method Laida criterion were adopted to process the missing data and inconsistent data.For damage identification,three machine learning techniques, including decision tree, random forest, K-Nearest- Neighbours (KNN) were adopted to identify the damage for multi-rotor drone and finally with the accuracy of 68.74%, 67.96%, 91.71%, respectively. Then, Convolutional Neural Networks (CNN), as the state-of-the-art machine learning technique also called deep learning was proposed and achieved outstanding success with 100% accuracy for damage identification. It is important to consider the parameter used in the CNN,so,in this research,the parameter used in the CNN,including sample length, convolution kernel, number of convolutional layer,activation function,batch-size,dropout,learning rate were analyzed by Python platform and the best parameter were selected.In summary, machine learning techniques can effectively detect damage for multi- rotor drone, however,CNN technique convolutional neural network possesses superior feature extraction capability and classification accuracy compared to traditional machine learning techniques. Flying-machines. Rotors. Neural networks (Computer science). 2023-09 Thesis http://psasir.upm.edu.my/id/eprint/111808/ http://psasir.upm.edu.my/id/eprint/111808/1/FK%202023%203%20IR.pdf text en public doctoral UPM Flying-machines. Rotors. Neural networks (Computer science). Mustapha, Faizal
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Mustapha, Faizal
topic	Flying-machines. Rotors. Neural networks (Computer science).
spellingShingle	Flying-machines. Rotors. Neural networks (Computer science). Ma, Yumeng Damage identification for multi-rotor drone using convolutional neural network technique
description	In contemporary society, multi-rotor drone have found extensive usage in various fields, such as agriculture, cargo transportation, and aerial photography. Damage to multi-rotor drone can compromise their safety and reduce efficiency. Therefore, early damage detection is crucial as it can prevent catastrophic failures and decrease the associated economic and human costs. At present, visual inspection is the primary method used for detecting damage in multi- rotor drone. However, this technique may not be entirely reliable in identifying minor faults that are difficult to discern with the naked eye. This study focuses on three experimental parts; firstly,to fabricate a multi-rotor drone as the research subject; secondly, to develop a vibration data acquisition device with MPU6050 and STM32 micro-controller,and thirdly,to identify the damage using machine learning techniques. Damage scenarios were set by releasing the bolts at different conjunction points of the multi-rotor body frame.Three damaged cases were set by releasing one bolt at arm conjunction, two bolts at arm conjunction and one bolt at motor conjunction, respectively. The first case (undamaged) is considered as the reference. Any change in structure can reflect in a vibration signal. Three axes vibration data were acquired under different conditions,for the sake of safety,the UAV was conducted under the ground with a idol motor speed. After the data collection,the data preprocessing techniques linear interpolation method Laida criterion were adopted to process the missing data and inconsistent data.For damage identification,three machine learning techniques, including decision tree, random forest, K-Nearest- Neighbours (KNN) were adopted to identify the damage for multi-rotor drone and finally with the accuracy of 68.74%, 67.96%, 91.71%, respectively. Then, Convolutional Neural Networks (CNN), as the state-of-the-art machine learning technique also called deep learning was proposed and achieved outstanding success with 100% accuracy for damage identification. It is important to consider the parameter used in the CNN,so,in this research,the parameter used in the CNN,including sample length, convolution kernel, number of convolutional layer,activation function,batch-size,dropout,learning rate were analyzed by Python platform and the best parameter were selected.In summary, machine learning techniques can effectively detect damage for multi- rotor drone, however,CNN technique convolutional neural network possesses superior feature extraction capability and classification accuracy compared to traditional machine learning techniques.
format	Thesis
qualification_level	Doctorate
author	Ma, Yumeng
author_facet	Ma, Yumeng
author_sort	Ma, Yumeng
title	Damage identification for multi-rotor drone using convolutional neural network technique
title_short	Damage identification for multi-rotor drone using convolutional neural network technique
title_full	Damage identification for multi-rotor drone using convolutional neural network technique
title_fullStr	Damage identification for multi-rotor drone using convolutional neural network technique
title_full_unstemmed	Damage identification for multi-rotor drone using convolutional neural network technique
title_sort	damage identification for multi-rotor drone using convolutional neural network technique
granting_institution	UPM
publishDate	2023
url	http://psasir.upm.edu.my/id/eprint/111808/1/FK%202023%203%20IR.pdf
_version_	1811767776828719104

Damage identification for multi-rotor drone using convolutional neural network technique

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