基于符号化表示相似度度量的卫星多元工程参数异常检测

宋华婷; 刘玉荣

doi:10.11728/cjss2023.01.220112004

基于符号化表示相似度度量的卫星多元工程参数异常检测

doi: 10.11728/cjss2023.01.220112004

宋华婷^{1, 2,},
刘玉荣¹

1.
中国科学院国家空间科学中心　北京　100190
2.
中国科学院大学　北京　100049

基金项目: 中国科学院战略性先导科技专项（A类）空间科学（二期）地面支撑系统科学卫星任务运控技术（XDA15040100）和空间先导专项科学卫星在轨运行维护科学卫星运控管理与服务（E02215A01S）共同资助

详细信息

作者简介:
宋华婷：E-mail：songhuating19@mails.ucas.ac.cn

中图分类号: TP391
计量
- 文章访问数: 126
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- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-12
- 录用日期: 2022-03-18
- 修回日期: 2022-11-10
- 网络出版日期: 2023-02-13

Multivariate Engineering Parameter Anomaly Detection of the Satellite Based on Similarity Metric of the Symbolic Representation

SONG Huating^{1, 2
,},
LIU Yurong¹

1.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 随着卫星系统复杂程度的日益增加，综合分析卫星多元参数之间的相关性异常对于卫星安全运行和空间任务的正确执行具有重要意义。利用某卫星工程参数数据，基于符号聚合近似算法（SAX），研究卫星多元工程参数的异常检测问题，解决了当前异常检测方法中多元参数融合时不考虑上下文信息造成信息丢失的问题，实现多元参数有效融合，形成一种优化的基于快速动态时间规整算法（Fast-DTW）的异常检测算法。研究结果表明，模型在某卫星电源子系统的异常检测过程中，recall，precision和F1 score分别为0.947，0.9和0.923，能够实际应用于卫星异常检测，提高卫星在轨运行的安全性。
- 异常检测 /
- 卫星工程参数 /
- 符号聚合近似 /
- 快速动态时间规整(Fast-DTW)
Abstract: The complexity of the satellite system is increasing, and the comprehensive analysis of the abnormal correlation between the satellite with multiple parameters is important for the safe operation of the satellite and the correct execution of space tasks. Aiming at the characteristics of large amount of engineering parameter data, high parameter correlation and pseudo-period, a satellite multivariate engineering parameter anomaly detection method based on symbolized representation similarity measurement is proposed. Using real satellite engineering parameter data, based on Symbol Aggregation Algorithm (SAX), this paper studies the anomaly detection problem of satellite multiple engineering parameters, solves the problem of the context information, realizes effective fusion, and forms an optimized anomaly detection algorithm based on similarity measurement Fast-DTW algorithm. Results show that during the abnormality detection process, the recall, precision and F1 score are 0.947, 0.9 and 0.923 respectively in the real satellite power subsystem, and the algorithm can be actually used in satellite anomaly detection to improve the safety of satellite in-orbit operation.
- Anomaly detection /
- Satellite engineering parameters /
- Symbolic aggregate approximation /
- Fast Dynamic Time Warping (Fast-DTW)

HTML全文

图 1 $ \varphi =4 $和$ \varphi =5 $时字母映射关系

Figure 1. Latter mapping relationship when $\varphi =4 $ and $\varphi =5 $

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图 2 某卫星电源分系统充电电流时序数据

Figure 2. Charging current timing data of a satellite power supply subsystem

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图 3 传统的固定窗口子序列分段算法的分段结果

Figure 3. Segmentation results of the traditional segmentation algorithm for fixed window opening sequence

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图 4 基于EMF卫星异常检测算法流程

Figure 4. Flow chart of satellite abnormality detection algorithm based on EMF

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图 5 特征选择流程

Figure 5. Feature selection flowchart

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图 6 $ {l}_{\mathrm{e}\mathrm{n}} $为5时EPA_Segment算法的分段结果

Figure 6. Segment results of EPA_Segment algorithm when $ {l}_{\mathrm{e}\mathrm{n}} $ is 5

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图 7 参数选择量对异常检测评价指标的影响

Figure 7. Effect of selectparametersnums on the evaluation index of anomaly detection

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图 8 参数训练过程中各评价指标结果

Figure 8. Results of each evaluation index during the parametric training process

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表 1 字母数为3～7的断点查找表

Table 1. Breakpoint lookup table with alphabetically numbered 3～7

	3	4	5	6	7
$ {\mathrm{\beta }}_{1} $	–0.43	–0.67	–0.84	–0.97	–1.07
$ {\mathrm{\beta }}_{2} $	0.43	0	–0.25	–0.43	–0.57
$ {\mathrm{\beta }}_{3} $	－	0.67	0.25	0	–0.18
$ {\mathrm{\beta }}_{4} $	－	－	0.84	0.43	0.18
$ {\mathrm{\beta }}_{5} $	－	－	－	0.97	0.57
$ {\mathrm{\beta }}_{6} $	－	－	－	－	1.07

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表 2 极值点获取（EPA）算法流程

Table 2. Extremum point acquisition algorithm process

Input: Time series $ T=\left({t}_{1},{t}_{2},\cdots .{t}_{n}\right) $, $ {\rm{threshold}} $

Initialize: The sliding window $ \omega ={T}_{{\rm{cycle}}}+{T}_{\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{d}}, $ 　　　　　　　 $ {t}_{i}={t}_{1} $

1.　while i<=n do

2. 　　 $ {t}_{i} $= maximum/mininum ($ \left[{t}_{i},{t}_{i+\omega }\right] $)

3. 　　 $ {t}_{j} $= maximum/mininum 　　　 ($ \left[{t}_{i+\omega -threshold},{t}_{i+\omega +threshold}\right] $)

4. 　　 Add $ i $ and $ j $ to the extreme point set $ {\rm{EPS}} $

5. 　　 Set $ i=j $

6.　 end while

7. return $ {\rm{EPS}} $

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表 3 分段（Segment）算法流程

Table 3. Segment algorithm process

Input: The extrepoint set sequence $ {\rm{EPS}}=({e}_{1},{e}_{2}, \cdots ,{e}_{m}) $, 　　Time series $ T=\left({t}_{1},{t}_{2},\cdots .{t}_{n}\right) $

Initialize: The sliding window size $ len $,$ {e}_{i}={e}_{1} $

1. 　while i<=m do

2. 　　 $ {t}_{{e}_{i}} $= maximum/mininum ($ \left[{t}_{{e}_{i}},{t}_{{l}_{en}+{e}_{i}}\right] $)

3. 　　 $ {t}_{{e}_{j}} $= maximum/mininum ($ \left[{t}_{{e}_{i}},{t}_{{l}_{en}+{e}_{i}}\right] $)

4. 　　 If $ {e}_{j}={e}_{i} $

5. 　　　 Add $ {e}_{i} $ to the set of segmentation time points $ {\rm{STP}} $

6. 　　 set $ {e}_{i}={e}_{j} $;

7.　 end while

8. ${\rm{STP}}=({s}_{1},{s}_{2},\cdots ,{s}_{k})$

9.　 while i<k do

10. 　　 Add $ \left[{t}_{{s}_{i}},{t}_{{s}_{i+1}}\right] $ to the set of pseudo-periodic 　　　　　subsequences $ {\rm{PPS}} $

11.　 end while

12. return $ {\rm{PPS}} $

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表 4 EPA_Segment算法调参优化结果

Table 4. Results of EPA_Segment tuning optimization algorithm

$ {l}_{\mathrm{e}\mathrm{n}} $	EPA_Segment (max)/ cycle	EPA_Segment (min) / cycle	Actual/ cycle
2	43	35	12
3	22	20	12
4	18	16	12
5	13	12	12
6	12	12	12
7	12	11	12
8	11	10	12

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表 5 电源分系统特征选择的最优结果

Table 5. Optimal result of the feature selection of the power supply subsystem

卫星参数	相关性百分比/（%）
测控应答机A机配电状态	83.5
测控应答机B机配电状态	83.3
GNSS接收机A机配电状态	83.0
备相位计配电状态	82.9
备载荷电控箱配电状态	82.8
相位计配电状态	82.8

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表 6 不同算法的异常检测结果

Table 6. Anomaly detection results for different algorithms

评价指标	EMF	1-NN ED	KMeans ED
Recall	0.947	0.871	0.833
Precision	0.9	0.9	0.714
F1 score	0.923	0.885	0.769

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