Akademik Veri Yönetim Sistemi
2nd International Conference on Innovative Research in Science Engineering and Technology, Rome, İtalya, 12 - 14 Eylül 2019
Sensor,
actuator and control surface faults in flight control systems can cause control
of the aircraft to be lost and aircraft accidents to happen. Hence, faults which
occur on sensor, actuator and control surface should be detected and isolated.
Methods like filters, observers, parameter estimation systems, parity equations
and signal models are used in literature for this purpose. First, fault detection
must be performed. Then, fault isolation must be performed and finally, problem
must be solved. Unmanned aerial vehicle (UAV) is used for a variety of purposes
which are photography, commercial, agricultural, search and rescue,
surveillance, military and many other areas. However, most UAV systems do not
have any fault detection and fault isolation scheme (FDI). During the control
of this vehicles, any sensor fault will cause to gather incorrect data first and
then will cause an accident and crash. By designing a FDI scheme, these
vehicles will be prevented from falling due to any sensor fault and also more
accurate control will be achieved. In this study, Artificial Neural Network
(ANN) was used to detect and isolate sensor fault occurring randomly on a UAV.
UAV flight data were used in the system design. Estimated sensor values were found
using ANN. Residual signals were analyzed and results were presented for fault
detection and isolation. MATLAB and LabVIEW softwares were used while scheduled
operations being performed.