School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
AVIC Aeronautical Radio Electronics Research Institute, Shanghai 201100, China.
Sensors (Basel). 2018 Nov 13;18(11):3916. doi: 10.3390/s18113916.
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.
无人驾驶飞行器 (UAV) 需要数据链路系统将地面数据终端与每架 UAV 的实时控制联系起来。因此，预测 UAV 数据链路系统健康状况的能力对于安全高效的操作至关重要。UAV 数据链路系统的性能受到硬件和 UAV 数据链路健康状况的影响。本文提出了一种基于贝叶斯网络融合潜在硬件设备故障和链路故障信息的方法，用于预测 UAV 数据链路系统的健康状态。我们的模型采用贝叶斯网络来描述与复杂多层次系统相关的信息和不确定性。为了预测 UAV 数据链路的健康状况，我们使用具有各种寿命特征的根节点设备的健康状况信息以及受误码率影响的链路健康状况信息。为了测试模型的有效性，我们测试了它对多级太阳能无人机数据链路系统健康状况的预测，结果表明该方法可以定量预测太阳能无人机数据链路系统的健康状况。研究结果可为提高 UAV 数据链路系统的可靠性提供指导，为准确预测 UAV 数据链路系统的健康状况奠定基础。
