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UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]

The design of Fault Detection and Diagnosis (FDD) is a tedious and challenging task. It is due to the changes and uncertainties associated with the aircraft dynamics following an occurrence of a fault. It was believed that until recently, the control reallocation following a system fault was too com...

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Main Authors: Sahwee, Zulhilmy, Mahmood, Aina Suriani, Abd. Rahman, Nazaruddin, Mohamed Sahari, Khairul Salleh
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018
Subjects:
Engineering mathematics. Engineering analysis
TJ Mechanical engineering and machinery
Online Access:http://ir.uitm.edu.my/id/eprint/40960/1/40960.pdf
http://ir.uitm.edu.my/id/eprint/40960/
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id my.uitm.ir.40960
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spelling my.uitm.ir.409602021-01-26T03:53:46Z http://ir.uitm.edu.my/id/eprint/40960/ UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.] Sahwee, Zulhilmy Mahmood, Aina Suriani Abd. Rahman, Nazaruddin Mohamed Sahari, Khairul Salleh Engineering mathematics. Engineering analysis TJ Mechanical engineering and machinery The design of Fault Detection and Diagnosis (FDD) is a tedious and challenging task. It is due to the changes and uncertainties associated with the aircraft dynamics following an occurrence of a fault. It was believed that until recently, the control reallocation following a system fault was too complex and computationally intensive for real world flight control cases. However, the recent, a dramatic improvement in computer speed and the development of more efficient algorithms have changed the situation considerably. This paper presents an artificial intelligent, in specific using Fuzzy Inference System method to detect an actuator fault. Three ground simulations were performed to validate the performances of the fault detection technique proposed. The residuals were evaluated by using three membership functions of the Fuzzy Inference System. The results show that the proposed technique was able to detect the actuator fault. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/40960/1/40960.pdf Sahwee, Zulhilmy and Mahmood, Aina Suriani and Abd. Rahman, Nazaruddin and Mohamed Sahari, Khairul Salleh (2018) UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]. Journal of Mechanical Engineering (JMechE), SI 5 (6). ISSN 18235514
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Engineering mathematics. Engineering analysis
TJ Mechanical engineering and machinery
spellingShingle Engineering mathematics. Engineering analysis
TJ Mechanical engineering and machinery
Sahwee, Zulhilmy
Mahmood, Aina Suriani
Abd. Rahman, Nazaruddin
Mohamed Sahari, Khairul Salleh
UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
description The design of Fault Detection and Diagnosis (FDD) is a tedious and challenging task. It is due to the changes and uncertainties associated with the aircraft dynamics following an occurrence of a fault. It was believed that until recently, the control reallocation following a system fault was too complex and computationally intensive for real world flight control cases. However, the recent, a dramatic improvement in computer speed and the development of more efficient algorithms have changed the situation considerably. This paper presents an artificial intelligent, in specific using Fuzzy Inference System method to detect an actuator fault. Three ground simulations were performed to validate the performances of the fault detection technique proposed. The residuals were evaluated by using three membership functions of the Fuzzy Inference System. The results show that the proposed technique was able to detect the actuator fault.
format Article
author Sahwee, Zulhilmy
Mahmood, Aina Suriani
Abd. Rahman, Nazaruddin
Mohamed Sahari, Khairul Salleh
author_facet Sahwee, Zulhilmy
Mahmood, Aina Suriani
Abd. Rahman, Nazaruddin
Mohamed Sahari, Khairul Salleh
author_sort Sahwee, Zulhilmy
title UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
title_short UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
title_full UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
title_fullStr UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
title_full_unstemmed UAV actuator fault detection through artificial intelligent technique / Zulhilmy Sahwee ... [et al.]
title_sort uav actuator fault detection through artificial intelligent technique / zulhilmy sahwee ... [et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2018
url http://ir.uitm.edu.my/id/eprint/40960/1/40960.pdf
http://ir.uitm.edu.my/id/eprint/40960/
_version_ 1690374298703757312
score 13.211869

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