基于遥测数据动态特征的卫星异常检测方法

李维铮; 孟桥

doi:10.11728/cjss2014.02.201

基于遥测数据动态特征的卫星异常检测方法

doi: 10.11728/cjss2014.02.201

李维铮^,,
孟桥

东南大学信息科学与工程学院南京 211189

基金项目: 航天飞行动力学技术重点实验室开放基金项目资助（2012afdl012）

详细信息

通讯作者:
李维铮,E-mail:liweizheng1992@gmail.com

中图分类号: V474
计量
- 文章访问数: 1491
- HTML全文浏览量: 85
- PDF下载量: 1704
- 被引次数: 0
出版历程
- 收稿日期: 2013-04-15
- 修回日期: 2013-09-10
- 刊出日期: 2014-03-15

Fault detection for in-orbit satellites using an adaptive prediction model

Li Weizheng^,,
Meng Qiao

School of Information Science and Engineering, Southeast University, Nanjing 211189

摘要

摘要: 基于遥测参数分析异常是保证卫星正常在轨运行的基础，通常采用阈值法判断遥测参数是否超差来判断卫星工作状态，由于其无法检测在阈值范围内变化的卫星遥测数据异常，因而会导致故障漏报.本文利用遥测参数动态变化特性，提出一种基于遥测数据变化规律检测异常的方法.利用周期图谱法求解遥测参数周期，根据遥测数据各周期之间参数值的相似性，按照遥测参数周期对数据进行采样，得到平稳差分序列，对其建立自回归移动平均混合模型，通过精确的预测结果与实测遥测数据比较来发现异常.利用该方法对实际在轨运行的某卫星2012年5月太阳能帆板转动异常故障进行验证，结果表明其能够有效避免故障漏报.
- 卫星故障检测 /
- 遥测数据 /
- 时间序列 /
- 周期图谱 /
- ARMA模型
Abstract: Faults are unavoidable for in-orbit satellites. They are able to be detected by carefully analyzing the telemetry data from these satellites. It is obvious that a good performance of anomaly detection algorithm is very important. This paper presents a new approach which takes the advantage of the similarity of preceding telemetry data to detect current anomaly. Because of its adaptive checking criteria, it can find some faults that cannot be found by some traditional methods. Most of important thing of this algorithm is that no prior knowledge about any particular satellite is needed except telemetry data themselves. In order to prove its availability, we use this new approach to process telemetry data from a China on-orbit satellite in 2012 and a fault is detected instantaneously.
- Satellite anomaly detection /
- Telemetry data /
- Time series /
- Periodogram spectral estimation /
- ARMA model

HTML全文

参考文献(11)

[1]	Zhang Xiaojie, Zhao Haitao, Miao Qiang. Reliability analysis of satellite system based on dynamic fault tree[J]. J. Astron., 2009, 30(3):1249-1254. In Chinese (张晓洁, 赵海涛, 苗强. 基于动态故障树的卫星系统可靠性分析[J]. 宇航学报, 2009, 30(3):1249-1254)
[2]	Jiang Lianxiang, Li Huawang, Yang Genqing. A survey of spacecraft autonomous fault diagnosis research[J]. J. Astron., 2009, 30(4):1320-1326. In Chinese (姜连祥, 李华旺, 杨根庆. 航天器自主故障诊断技术研究进展[J]. 宇航学报, 2009, 30(4):1320-1326)
[3]	Michael J C, David E M, Thomas J S, et al. Comparison of electronics reliability assessment approaches[J]. IEEE Trans. Reliab., 1993, 42(4):542-546
[4]	Li Xiaolei, Han Jiawei, Sangkyum K. Motion-alert, automatic anomaly detection in massive moving objects[J]. Lect. Notes Comput. Sci., 2006, 3975:166-177
[5]	Lagunas M A. The periodogram envelope in parametric and non-parametric spectral estimation[C]//International Conference on Acoustics, Speech, and Signal Processing. Glasgow: IEEE, 1989
[6]	Wang Zhenhua, Zhang Miao, Shen Yi. Actuator fault detection and isolation for the attitude control system of satellite[J]. J. Harbin Inst. Technol., 2013, 45(2):72-76. In Chinese (王振华, 张淼, 沈毅. 卫星姿态控制系统执行器故障检测与分离[J]. 哈尔滨工业大学学报, 2013, 45(2):72-76)
[7]	Niu Erzhuo, Wang Qing, Dong Chaoyang. Robust fault detection of a class of network control system for flight vehicle[J]. J. Astron., 2012, 33(12):1736-1741. In Chinese (牛尔卓, 王青, 董朝阳. 一类飞行器网络控制系统的鲁棒故障检测算法[J]. 宇航学报, 2011, 33(12):1736-1741)
[8]	Navarro-Moreno J. ARMA prediction of widely linear systems by using the innovations algorithm[J]. IEEE Trans. Signal Proc., 2008, 34(2):3061-3068
[9]	Chen Zhengsheng, Lü Zhiping, Zhang Qinghua, Wang Peng. GPS satellite clock error predicting based on ARMA analysis[J]. Sci. Surv. Mapp., 2011, 36(3):123-128
[10]	Li Ruiying, Kang Rui. Research on failure rate forecasting method based oil ARMA model[J]. Syst. Eng. Electron., 2008, 45(30):345-351
[11]	Celenk M. Predictive network anomaly detection and visualization[J].IEEE Trans. Inf. Forens. Secur., 2010, 35(2):288-299