Abstract
Abstract
In this work, we propose a new hybrid approach to optimize the reliability of Unmanned Aerial Vehicles (UAV) based on two complementary approaches. The first approach is based on the Failure Mode and Effects Analysis (FMEA), which is an informal method based on the establishment of a table describing the failure modes of the elements of the system and their effects, the aim of using this method is to optimize the search of critical scenarios that leads the system to the failure state. The second approach is based on Developed Stochastic Petri nets formalism coupled with the reliability laws (RLSPNs) according to the nature of the components (electronic, electrical, mechanical, software, …etc.) of the system, this simplifies the reliability evaluation of the system components as well as the reliability of the complete system. The goal of this approach is to determine the main causes of the failure of the system, optimize the search of critical scenarios, and study the reliability of system components; in order to have the most reliable and least reliable components. This detailed study will certainly make it possible to propose improvements that can in the future improve the reliability of the complete system.
Publisher
Research Square Platform LLC
Reference44 articles.
1. Strat R, Strat R, Strat R (2010) Les drones tactiques à voilure tournante dans les engagements contemporains. Rech Doc
2. Direction Générale des politiques Internes - Département thématique C: Droits des citoyens et affaires Constitutionnelles, Libertés civiles, justice, et affaires intérieures (2015) Les conséquences de l’usage civil des drones sur la protection de la vie privée et des données à caractère personnel. Dans le site www Eur Eur eu 1–37
3. State-of-the-art technologies for UAV inspections;Jordan S;IET Radar Sonar Navig,2018
4. MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography;Zhao LL;Drones,2023
5. Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types;Yan J;Drones,2023