Next Article in Journal
Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay
Next Article in Special Issue
Proactive Coverage Area Decisions Based on Data Field for Drone Base Station Deployment
Previous Article in Journal
Robust Iterative Distributed Minimum Total MSE Algorithm for Secure Communications in the Internet of Things Using Relays
Previous Article in Special Issue
From the Eye of the Storm: An IoT Ecosystem Made of Sensors, Smartphones and UAVs
Open AccessArticle

Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
AVIC Aeronautical Radio Electronics Research Institute, Shanghai 201100, China
3
Unmanned System Institute, Beihang University, Beijing 100191, China
4
Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3916; https://doi.org/10.3390/s18113916
Received: 20 September 2018 / Revised: 7 November 2018 / Accepted: 7 November 2018 / Published: 13 November 2018
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
Unmanned aerial vehicles (UAVs) require data-link system to link ground data terminals to the real-time controls of each UAV. Consequently, the ability to predict the health status of a UAV data-link system is vital for safe and efficient operations. The performance of a UAV data-link system is affected by the health status of both the hardware and UAV data-links. This paper proposes a method for predicting the health state of a UAV data-link system based on a Bayesian network fusion of information about potential hardware device failures and link failures. Our model employs the Bayesian network to describe the information and uncertainty associated with a complex multi-level system. To predict the health status of the UAV data-link, we use the health status information about the root node equipment with various life characteristics along with the health status of the links as affected by the bit error rate. In order to test the validity of the model, we tested its prediction of the health of a multi-level solar-powered unmanned aerial vehicle data-link system and the result shows that the method can quantitatively predict the health status of the solar-powered UAV data-link system. The results can provide guidance for improving the reliability of UAV data-link system and lay a foundation for predicting the health status of a UAV data-link system accurately. View Full-Text
Keywords: UAV data-link system; Bayesian networks; health status prediction; networking mode; bit error rate UAV data-link system; Bayesian networks; health status prediction; networking mode; bit error rate
Show Figures

Figure 1

MDPI and ACS Style

Wang, X.; Guo, H.; Wang, J.; Wang, L. Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network. Sensors 2018, 18, 3916.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop