UAV actuator fault detection through artificial intelligent technique

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 Z., Mahmood A.S., Rahman N.A., Sahari K.S.M.
Other Authors: 55524079500
Format: Article
Published: UiTM Press 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-241802023-05-29T14:56:28Z UAV actuator fault detection through artificial intelligent technique Sahwee Z. Mahmood A.S. Rahman N.A. Sahari K.S.M. 55524079500 57193427529 9338388000 57218170038 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. � 2018 Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Final 2023-05-29T06:56:28Z 2023-05-29T06:56:28Z 2018 Article 2-s2.0-85052592995 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052592995&partnerID=40&md5=4d9c0f5feb07c7e76030497f09c230ab https://irepository.uniten.edu.my/handle/123456789/24180 5 Specialissue6 141 154 UiTM Press Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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. � 2018 Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
author2 55524079500
author_facet 55524079500
Sahwee Z.
Mahmood A.S.
Rahman N.A.
Sahari K.S.M.
format Article
author Sahwee Z.
Mahmood A.S.
Rahman N.A.
Sahari K.S.M.
spellingShingle Sahwee Z.
Mahmood A.S.
Rahman N.A.
Sahari K.S.M.
UAV actuator fault detection through artificial intelligent technique
author_sort Sahwee Z.
title UAV actuator fault detection through artificial intelligent technique
title_short UAV actuator fault detection through artificial intelligent technique
title_full UAV actuator fault detection through artificial intelligent technique
title_fullStr UAV actuator fault detection through artificial intelligent technique
title_full_unstemmed UAV actuator fault detection through artificial intelligent technique
title_sort uav actuator fault detection through artificial intelligent technique
publisher UiTM Press
publishDate 2023
_version_ 1806423522386378752