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

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## Abstract

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## 1. Introduction

## 2. Overview of Bayesian Networks

- Define node variables;
- Connect the node variables through the directed edges;
- Establish a CPT for the non-root node.

## 3. Model for Predicting the Health of an Unmanned Aerial Vehicle Data-Link System

#### 3.1. Constructing a Bayesian Network Root Node Prediction Model

#### 3.2. A Bayesian Network Model of an Unmanned Aerial Vehicle Data-Link System Considering the Networking Mode

#### 3.3. A Bayesian Network Model of an Unmanned Aerial Vehicle Data-Link System Considering the Bit Error Rate

- Determining nodes of the UAV data-link system;
- Construct a DAG of the UAV data-link system and establish the CPT of the non-root nodes;
- Consider the impact of bit error rate, add the bit error rate nodes and establish the CPT;
- The JT estimation algorithm is used to solve the joint probability of relevant nodes, to update the conditional probability values of each node and to achieve the deduction of state probability of UAV data-link system nodes.

## 4. Case Study

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**5-node directed acyclic graph and conditional probability table. (

**a**) 5-node directed acyclic graph; (

**b**) Conditional probability table.

**Figure 2.**The procedure of Junction Tree (JT) algorithm and build thought of JT. DAG: directed acyclic graph.

**Figure 4.**Directed acyclic graph of UAV data-link. LOS: line-of-sight; NLOS: non-line-of-sight; UAV-RS: UAV repeater satellite; G-RS: ground data terminal repeater satellite.

**Figure 5.**Simulation of bit error rate under different bit stream rates, transmission symbols and signal to noise ratio.

**Figure 8.**Communication distance distribution curve of each link of solar-powered UAV data-link system. (

**a**) Communication distance of UAV-Satellite links; (

**b**) Communication distance of ground-satellite links.

**Figure 11.**Health state probability prediction curve of Solar-powered UAV data-link intermediate node future 840 h.

**Figure 12.**Health state probability prediction curve of Solar-powered UAV data-link intermediate node future 840 h (Continued).

**Figure 13.**Health state probability prediction curve of Solar-powered UAV data-link leaf node future 840 h.

Node | Description of the Prediction Model | Prediction Model and Parameter |
---|---|---|

$\alpha $-chain airborne data terminal | degradation model for power MOSFET | $Y={e}^{\left({a}_{1}+{a}_{2}\left(\frac{1}{T}-\frac{1}{298}\right)\right)}\cdot \left({t}^{{a}_{3}+{a}_{4}\ast \frac{T}{298}}-1\right)+Yo$ $\begin{array}{l}Ea=0.616,A=7.79\times {10}^{5},{Y}_{0}=5,L=8,\sigma =0.08\\ ({a}_{1}=-8.0363,{a}_{2}=-5529.6,{a}_{3}=-0.019,{a}_{4}=-0.8395)\end{array}$ |

$\alpha $-chain repeater satellite | Wiener process, Arrhenius model | $Ea=0.616,A=7.79\times {10}^{5},{Y}_{0}=5,L=8,\sigma =0.08$ |

$\alpha $-chain ground data terminal | exponential distribution | ${R}_{3}(t)=\mathrm{exp}\left[-\left(\raisebox{1ex}{$t$}\!\left/ \!\raisebox{-1ex}{$2600$}\right.\right)\right]$ |

$\mathrm{A}$-chain airborne data terminal | degradation model for power MOSFET model | ${a}_{1}=-7.1342,{a}_{2}=-5391.4,{a}_{3}=-0.022,{a}_{4}=-0.8411$ |

$\mathrm{A}$-chain ground data terminal | exponential distribution | ${R}_{5}(t)=\mathrm{exp}\left[-\left(\raisebox{1ex}{$t$}\!\left/ \!\raisebox{-1ex}{$3500$}\right.\right)\right]$ |

$\mathrm{B}$-chain airborne data terminal | Combined acceleration model | ${a}_{1}=-7.8220,{a}_{2}=-5419.2,{a}_{3}=-0.023,{a}_{4}=-0.8317$ |

$\mathrm{B}$-chain ground data terminal | exponential distribution | ${R}_{7}(t)=\mathrm{exp}\left[-\left(\raisebox{1ex}{$t$}\!\left/ \!\raisebox{-1ex}{$4500$}\right.\right)\right]$ |

$\mathsf{\beta}$-chain repeater satellite | Wiener process, Arrhenius model | $Ea=0.64,A=2.115\times {10}^{6},{Y}_{0}=33,L=36.5,\sigma =0.178$ |

$\mathsf{\beta}$-chain ground data terminal | exponential distribution | ${R}_{s}(t)=\mathrm{exp}\left[-\left(\raisebox{1ex}{$t$}\!\left/ \!\raisebox{-1ex}{$4500$}\right.\right)\right]$ |

$\mathrm{C}$-chain airborne data terminal | degradation model for power MOSFET | ${a}_{1}=-8.2334,{a}_{2}=-5219.5,{a}_{3}=-0.014,{a}_{4}=-0.7991$ |

$\mathrm{C}$-chain ground data terminal | exponential distribution | ${R}_{9}=\mathrm{exp}\left[-\left(\raisebox{1ex}{$t$}\!\left/ \!\raisebox{-1ex}{$4000$}\right.\right)\right]$ |

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**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.
https://doi.org/10.3390/s18113916

**AMA 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(11):3916.
https://doi.org/10.3390/s18113916

**Chicago/Turabian Style**

Wang, Xiaohong, Hongzhou Guo, Jingbin Wang, and Lizhi Wang.
2018. "Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network" *Sensors* 18, no. 11: 3916.
https://doi.org/10.3390/s18113916