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论文:2018,Vol:36,Issue(1):20-27 |
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引用本文： |
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姜海旭, 张科, 王靖宇, 吕梅柏. 基于形态变分模态分解和JRD的航天器异常状态识别[J]. 西北工业大学学报 |
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Jiang Haixu, Zhang Ke, Wang Jingyu, Lü Meibo. Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD[J]. Northwestern polytechnical university |
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| 基于形态变分模态分解和JRD的航天器异常状态识别 |
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| 姜海旭1,2, 张科1,2, 王靖宇1,2, 吕梅柏1,2 |
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1. 西北工业大学 航天学院, 陕西 西安 710072;
2. 西北工业大学 航天飞行动力学国家级重点实验室, 陕西 西安 710072 |
| 摘要: |
| 针对在轨航天器微弱异常难以识别问题，提出了一种基于形态变分模态分解和JRD距离的异常状态识别方法。首先利用形态变分模态分解将航天器遥测数据分解为不同频率的多尺度模态分量，然后提取各模态分量的Rényi熵，将得到的Rényi熵向量作为遥测数据的特征，最后通过计算样本数据与实测数据特征之间的JRD距离，实现对航天器异常状态的识别。以某卫星反作用轮转微弱异常转速实测数据对该方法进行仿真验证，仿真结果表明，提出的识别方法能够有效识别航天器异常状态，且在识别速度上具有明显优势。 |
| 关键词:
形态变分模态分解
Rényi熵
JRD距离
异常识别
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| Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD |
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| Jiang Haixu1,2, Zhang Ke1,2, Wang Jingyu1,2, Lü Meibo1,2 |
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1. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed. |
| Key words:
M-VMD
Rényi entropy
Jensen-Rényi divergence
anomaly recognition
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| 收稿日期: 2017-04-28
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金（61502391、61174204）资助 |
| 通讯作者:
Email： |
| 作者简介: 姜海旭(1984-),西北工业大学博士研究生,主要从事数据的故障预测、诊断研究。
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