Fault Detection in Quadrotor MAV

Unmanned Aerial Vehicle (UAV) is being used in a wide range of human life. Researcher preferred quadrotor as it can be brought into the first generation of simulator map of an aircraft. It can be developed into larger manned flight. In this regard, extensive research in Fault detection (FD) is nece...

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Main Authors:	Chan, Shi Jing, Pebrianti, Dwi, Goh, Ming Qian, Bayuaji, Luhur
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/1/fkee-2017-dwi-fault%20detection%20in%20quadrotor%20mav1.pdf

id	ump-19524
recordtype	eprints
spelling	ump-195242020-02-21T03:58:05Z http://umpir.ump.edu.my/id/eprint/19524/ Fault Detection in Quadrotor MAV Chan, Shi Jing Pebrianti, Dwi Goh, Ming Qian Bayuaji, Luhur TK Electrical engineering. Electronics Nuclear engineering Unmanned Aerial Vehicle (UAV) is being used in a wide range of human life. Researcher preferred quadrotor as it can be brought into the first generation of simulator map of an aircraft. It can be developed into larger manned flight. In this regard, extensive research in Fault detection (FD) is necessary, so that it can enhance its safety features. FD is designed to respond and to exclude the wrong information and to quickly perceive and shoulder important regulation. The proposed method for the fault detection in this study uses hybrid technique which combines the Kalman filter and Artificial Neural Network (ANN). Two classes of approaches are analyzed: the system identification approach using ANN and the observer-based approach using Kalman filter. A representative Artificial Neural Network (ANN) model has been designed and used to simulate the system behaviors under various failure conditions. The Kalman filter recognizes data from sensors and indicates the fault of the system in sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to ANN, which consists of a bank of parameter estimation that generates failure state. The result of the residual signal before filtered and after filtered showed that Kalman-ANN is able to identify multi fault and immediately correct the system to the normal state. The accuracy of the detection is 85 percent. The proposed method is able to detect fault in a short time with delay of 9.23E-05 seconds. IEEE 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19524/1/fkee-2017-dwi-fault%20detection%20in%20quadrotor%20mav1.pdf Chan, Shi Jing and Pebrianti, Dwi and Goh, Ming Qian and Bayuaji, Luhur (2017) Fault Detection in Quadrotor MAV. In: 7th IEEE International Conference on System Engineering and Technology (ICSET 2017), 2-3 October 2017 , Shah Alam, Malaysia. pp. 65-70.. ISSN 2470-640X http://dx.doi.org/10.1109/ICSengT.2017.8123422 doi:10.1109/ICSengT.2017.8123422
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Chan, Shi Jing Pebrianti, Dwi Goh, Ming Qian Bayuaji, Luhur Fault Detection in Quadrotor MAV
description	Unmanned Aerial Vehicle (UAV) is being used in a wide range of human life. Researcher preferred quadrotor as it can be brought into the first generation of simulator map of an aircraft. It can be developed into larger manned flight. In this regard, extensive research in Fault detection (FD) is necessary, so that it can enhance its safety features. FD is designed to respond and to exclude the wrong information and to quickly perceive and shoulder important regulation. The proposed method for the fault detection in this study uses hybrid technique which combines the Kalman filter and Artificial Neural Network (ANN). Two classes of approaches are analyzed: the system identification approach using ANN and the observer-based approach using Kalman filter. A representative Artificial Neural Network (ANN) model has been designed and used to simulate the system behaviors under various failure conditions. The Kalman filter recognizes data from sensors and indicates the fault of the system in sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to ANN, which consists of a bank of parameter estimation that generates failure state. The result of the residual signal before filtered and after filtered showed that Kalman-ANN is able to identify multi fault and immediately correct the system to the normal state. The accuracy of the detection is 85 percent. The proposed method is able to detect fault in a short time with delay of 9.23E-05 seconds.
format	Conference or Workshop Item
author	Chan, Shi Jing Pebrianti, Dwi Goh, Ming Qian Bayuaji, Luhur
author_facet	Chan, Shi Jing Pebrianti, Dwi Goh, Ming Qian Bayuaji, Luhur
author_sort	Chan, Shi Jing
title	Fault Detection in Quadrotor MAV
title_short	Fault Detection in Quadrotor MAV
title_full	Fault Detection in Quadrotor MAV
title_fullStr	Fault Detection in Quadrotor MAV
title_full_unstemmed	Fault Detection in Quadrotor MAV
title_sort	fault detection in quadrotor mav
publisher	IEEE
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/ http://umpir.ump.edu.my/id/eprint/19524/1/fkee-2017-dwi-fault%20detection%20in%20quadrotor%20mav1.pdf
first_indexed	2023-09-18T22:27:54Z
last_indexed	2023-09-18T22:27:54Z
_version_	1777416089581387776

Fault Detection in Quadrotor MAV

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