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

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


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.


Item Type: Article
Email / ID Num.
Sahwee, Zulhilmy
Mahmood, Aina Suriani
Abd. Rahman, Nazaruddin
Mohamed Sahari, Khairul Salleh
Subjects: T Technology > TA Engineering. Civil engineering > Engineering mathematics. Engineering analysis
T Technology > TJ Mechanical engineering and machinery
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering (JMechE)
UiTM Journal Collections: UiTM Journal > Journal of Mechanical Engineering (JMechE)
ISSN: 18235514
Volume: SI 5
Number: 6
Keywords: Fault Detection; actuator fault; fuzzy inference system.
Date: 2018
Edit Item
Edit Item


[thumbnail of 40960.pdf] Text

Download (1MB)

ID Number




Statistic details