Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network
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 affe...
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/11/3916 |
| _version_ | 1818011298068168704 |
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| author | Xiaohong Wang Hongzhou Guo Jingbin Wang Lizhi Wang |
| author_facet | Xiaohong Wang Hongzhou Guo Jingbin Wang Lizhi Wang |
| author_sort | Xiaohong Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:06:01Z |
| format | Article |
| id | doaj.art-cbd7a60cdee54b209eae5bd81f0fc7fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:06:01Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cbd7a60cdee54b209eae5bd81f0fc7fa2022-12-22T02:08:32ZengMDPI AGSensors1424-82202018-11-011811391610.3390/s18113916s18113916Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian NetworkXiaohong Wang0Hongzhou Guo1Jingbin Wang2Lizhi Wang3School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaAVIC Aeronautical Radio Electronics Research Institute, Shanghai 201100, ChinaUnmanned System Institute, Beihang University, Beijing 100191, ChinaUnmanned 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.https://www.mdpi.com/1424-8220/18/11/3916UAV data-link systemBayesian networkshealth status predictionnetworking modebit error rate |
| spellingShingle | Xiaohong Wang Hongzhou Guo Jingbin Wang Lizhi Wang Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network Sensors UAV data-link system Bayesian networks health status prediction networking mode bit error rate |
| title | Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network |
| title_full | Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network |
| title_fullStr | Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network |
| title_full_unstemmed | Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network |
| title_short | Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network |
| title_sort | predicting the health status of an unmanned aerial vehicles data link system based on a bayesian network |
| topic | UAV data-link system Bayesian networks health status prediction networking mode bit error rate |
| url | https://www.mdpi.com/1424-8220/18/11/3916 |
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