# Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads

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## Abstract

**:**

## 1. Introduction

^{2}) [5,6]. Time variations and extremely high thermal loads can affect satellite health and even lead to satellite system failures [7,8]. Therefore, the rapid and effective dynamic health assessment of satellites is of great significance.

## 2. Dynamic Health Intelligent Evaluation Model

#### 2.1. Principle Description and Evaluation Index

#### 2.1.1. Definition of Evaluation Index

- Failure Probability $\xi (t)$:

- Health Degree $H(t)$:

- Failure Factor $F(t)$:

#### 2.1.2. Principle Description

#### 2.2. Component Temperature Dynamic Modeling

#### 2.3. Component Failure Probability Fuzzy Modeling

#### 2.3.1. Fuzzy Reasoning

#### 2.3.2. Fuzzy Rule Design

#### 2.4. System Health Dynamic Evaluation Modeling

## 3. Results and Discussion

#### 3.1. Cases Design

#### 3.1.1. Parameters Setting for Normal Orbital Operation

#### 3.1.2. Design of Thermal Damage Conditions

^{2}in an impulse cycle, and the remaining heat load of 50 s is 0 W/cm

^{2}.

^{2}and 4 W/cm

^{2}. In the initial moment of simulation, the target temperature controller is loaded with extreme thermal loads and the satellite thermal health response is solved by using the THIEM.

#### 3.2. Effects of Thermal Load Amplitude on Satellite Health

^{2}and 4 W/cm

^{2}respectively, the health degree and failure factors of the satellite subsystems are shown in Figure 8 and Figure 9. As shown in Figure 8a and Figure 9a, the health degree of the satellite’s thermal control system is exponentially decreasing, and the health degree of the satellite’s payload is also slightly damaged. This is because the thermal damage of the temperature controller component will directly affect the health of the thermal control subsystem. As mentioned earlier, the temperature controller is designed to serve the payload, so when the controller is damaged, the payload health is also affected. As shown in Figure 8b and Figure 9b, the failure factor of the satellite thermal control subsystem increased. According to the definition of failure factor, failure factor is not equal to 0, which means that a component will fail at that time. Therefore, the distribution of the failure factor can determine the speed of failure.

#### 3.3. Effects of Thermal Load Duty Ratio on Satellite Health

#### 3.4. Effects of Thermal Load Cycle on Satellite Health

## 4. Conclusions

- The fuzziness of the relationship between temperature and failure probability is considered, and the relationship between temperature and failure probability is quantitatively described by intelligent analysis method (fuzzy reasoning).
- The model can quickly and accurately evaluate the effects of different thermal conditions on satellite health. The health deterioration of the system is characterized by the change of health degree and failure factor.
- Multi-period, high heat flux density, and low duty ratio have great influence on satellite health.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**The principle of satellite dynamic health intelligent evaluation model: (

**a**) Model schematic; (

**b**) Topological system (satellite-subsystems-components).

**Figure 8.**Health indicators of satellite subsystems (Case I): (

**a**) Health degree. (

**b**) Failure factor.

**Figure 9.**Health indicators of satellite subsystems (Case II): (

**a**) Health degree. (

**b**) Failure factor.

**Figure 10.**Health indicators of thermal control subsystem (TCS) (Compare Case I and Case II): (

**a**) Health degree. (

**b**) Failure factor.

**Figure 12.**Health indicators of satellite subsystems (Case III). (

**a**) Health degree. (

**b**) Failure factor.

**Figure 13.**Health indicators of TCS (Compare Case I and Case III). (

**a**)Health degree. (

**b**)Failure factor.

**Figure 15.**Health indicators of satellite subsystems (Case IV). (

**a**) Health degree. (

**b**) Failure factor.

**Figure 16.**Health indicators of TCS (Compare Case I and Case IV). (

**a**)Health degree. (

**b**)Failure factor.

(a) | (b) | ||||

Fuzzy Sets | Ranks | Linguistic Values | Fuzzy Sets | Ranks | Linguistic Values |

NB | −4 | Negative big | ZE | 0 | Zero |

NM | −3 | Negative medium | PZ1 | 1 | Positive zero 1 |

NS | −2 | Negative small | PZ2 | 2 | Positive zero 2 |

NZ | −1 | Negative zero | PS1 | 3 | Positive small 1 |

ZE | 0 | Zero | PS2 | 4 | Positive small 2 |

PZ | 1 | Positive zero | PM1 | 5 | Positive medium 1 |

PS | 2 | Positive small | PM2 | 6 | Positive medium 2 |

PM | 3 | Positive medium | PB1 | 7 | Positive big 1 |

PB | 4 | Positive big | PB2 | 8 | Positive big 2 |

Parameters | Value |
---|---|

Solar incident angle | 17.23 °C |

Orbit altitude | 641.65 km |

Average of solar radiation | 1354 W/m^{2} |

Albedo | 0.35 |

Earth infrared radiation | 221.484 W/m^{2} |

Space temperature | 4 K |

Cases | Amplitude | Duty Ratio | Cycle |
---|---|---|---|

I | 2 W/cm^{2} | 1/6 (Heat 10 s; Cool 50 s) | 10 |

II | 4 W/cm^{2} | 1/6 (Heat 10 s; Cool 50 s) | 10 |

III | 2 W/cm^{2} | 1/2 (Heat 10 s; Cool 10 s) | 10 |

IV | 2 W/cm^{2} | 1/6 (Heat 10 s; Cool 50 s) | 40 |

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**MDPI and ACS Style**

Li, E.-H.; Li, Y.-Z.; Li, T.-T.; Li, J.-X.; Zhai, Z.-Z.; Li, T.
Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads. *Entropy* **2019**, *21*, 983.
https://doi.org/10.3390/e21100983

**AMA Style**

Li E-H, Li Y-Z, Li T-T, Li J-X, Zhai Z-Z, Li T.
Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads. *Entropy*. 2019; 21(10):983.
https://doi.org/10.3390/e21100983

**Chicago/Turabian Style**

Li, En-Hui, Yun-Ze Li, Tian-Tian Li, Jia-Xin Li, Zhuang-Zhuang Zhai, and Tong Li.
2019. "Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads" *Entropy* 21, no. 10: 983.
https://doi.org/10.3390/e21100983