Please wait a minute...
浙江大学学报(工学版)  2020, Vol. 54 Issue (4): 824-832    DOI: 10.3785/j.issn.1008-973X.2020.04.022
航空航天技术     
皮纳卫星姿控系统分层式故障检测
费云(),蒙涛*(),金仲和
浙江大学 航空航天学院,浙江 杭州 310027
Hierarchical fault detection for nano-pico satellite attitude control system
Yun FEI(),Tao MENG*(),Zhong-he JIN
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
 全文: PDF(1086 KB)   HTML
摘要:

为了实现皮纳卫星的多故障在线检测,提出针对姿控系统的分层故障检测方案. 该方案将系统划分为系统层和器件层,系统层基于卫星动力学与运动学模型设计非线性观测器,实现姿控分系统故障的全局监测;器件层利用动力学模型设计数字动力学陀螺,结合卡尔曼算法新息以及小波分析,实现故障的定位. 通过分层检测,可以支持多器件故障的实时检测,能够检测常见的在轨姿态控制系统故障. 仿真结果表明,该方案能够实现多器件同时故障的检测,适应突变、偏差、恒增益、输出卡死等故障类型,检测准确率达到92%,误检率低于2%;由于采用假设检验取代阈值判断,相对于常规小波阈值检测方法,故障检测结果的可靠性更高,适应更多的故障类型,避免了阈值的选取问题,且节省计算资源,无需大量的历史信息,能够满足在线实时检测要求.

关键词: 皮纳卫星在线检测分层检测多器件故障小波变换    
Abstract:

A hierarchical fault detection scheme for attitude control system was proposed in order to realize the multi-fault online detection of nano-pico satellite. The system was divided into system layer and component layer. The system layer designed nonlinear observer based on satellite dynamics and kinematics model to realize the global monitoring of attitude control system faults. The component layer used dynamic model to design digital dynamic gyro. The fault location can be realized combined with the residual of Kalman algorithm and wavelet analysis. Multi-component faults can be detected in real time through hierarchical detection. The common on-orbit faults of the attitude control system can be detected. The simulation results show that the scheme can detect simultaneous faults of multiple components, and adapt to fault types such as sudden change, deviation, constant gain, output stuck, etc. The rate of fault detection accuracy reaches 92%, and the false detection rate is less than 2%. Due to the use of hypothesis testing instead of threshold judgment, the fault detection result is more reliable compared with the conventional wavelet detection method. The proposed scheme adapts to more fault types and avoids problems of threshold selection. The scheme saves computing resources, does not need a lot of historical information, and can meet the requirements of online real-time detection.

Key words: nano-pico satellite    online detection    hierarchical detection    multi-component fault    wavelet transform
收稿日期: 2019-01-14 出版日期: 2020-04-05
CLC:  V 448  
基金资助: 国家自然科学基金资助项目(61503334);省级重点研发计划资助项目(209C05004)
通讯作者: 蒙涛     E-mail: fy927@zju.edu.cn;mengtao@zju.edu.cn
作者简介: 费云(1993—),男,博士生,从事微小卫星故障诊断的研究. orcid.org/0000-0003-2789-7404. E-mail: fy927@zju.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
费云
蒙涛
金仲和

引用本文:

费云,蒙涛,金仲和. 皮纳卫星姿控系统分层式故障检测[J]. 浙江大学学报(工学版), 2020, 54(4): 824-832.

Yun FEI,Tao MENG,Zhong-he JIN. Hierarchical fault detection for nano-pico satellite attitude control system. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 824-832.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.04.022        http://www.zjujournals.com/eng/CN/Y2020/V54/I4/824

图 1  姿控系统组成框图
图 2  分层检测方案
敏感器参数 参数值
陀螺(角度随机游走)nA 10?6 (°)/s1/2
陀螺(角速度随机游走)nR 10?8 (°)/s3/2
星敏感器噪声标准差σs [0.02, 0.003, 0.003]°
模拟太阳敏感器噪声标准差σsun 0.5 °
磁强计噪声标准差σm 10?8 T
表 1  敏感器模型参数
故障场景 故障器件 故障类型 故障时间/s
场景1 陀螺仪 突变故障 51
场景1 星敏感器 恒偏差故障 51
场景2 模拟太敏 无输出故障 112
场景2 磁强计 恒增益故障 112
场景2 飞轮 停转 112
表 2  故障场景说明
图 3  场景-系统层检测结果
图 4  场景-器件层检测结果
图 5  场景2的系统层检测结果
图 6  场景2的器件层检测结果
磁强计故障类型 状态 分层检测 小波阈值检测
故障 正常 故障 正常
卡死故障 故障 129 1 9 23
卡死故障 正常 21 149 141 127
偏差故障 故障 132 3 146 18
偏差故障 正常 18 147 4 132
增益故障 故障 144 0 147 31
增益故障 正常 6 150 3 119
表 3  2种检测方案的统计结果
故障类型 分层检测 常规小波阈值检测
准确率 误检率 漏检率 准确率 误检率 漏检率
卡死 92.7% 0.6% 14% 45% 15% 94%
偏差 93% 2% 12% 93% 12% 3%
增益 98% 0% 4% 89% 21% 2%
表 4  2种方案的检测效率对比
1 闻新, 张兴旺, 秦钰琦, 等 国外航天器在轨故障模式统计与分析[J]. 质量与可靠性, 2014, (6): 13- 18
WEN Xin, ZHANG Xing-wang, QIN Yu-qi, et al Statistics and analysis of on-orbit failure modes of foreign spacecraft[J]. Quality and Reliability, 2014, (6): 13- 18
2 谭春林, 胡太彬, 王大鹏, 等 国外航天器在轨故障统计与分析[J]. 航天器工程, 2011, 20 (4): 130- 136
TAN Chun-lin, HU Tai-bin, WANG Da-peng, et al Analysis on foreign spacecraft in-orbit failures[J]. Spacecraft Engineering, 2011, 20 (4): 130- 136
doi: 10.3969/j.issn.1673-8748.2011.04.029
3 闻竞竞, 黄道. 故障诊断方法综述[C]// 计算机技术与应用进展. 合肥: 中国科学技术大学出版社, 2007.
WEN Jing-jing, HUANG Dao. Review on fault diagnosis [C]//Advances in Computer Technology and Applications. Hefei: Press of University of Science and Technology of China, 2007.
4 谢敏, 楼鑫, 罗芊 航天器故障诊断技术综述及发展趋势[J]. 软件, 2016, 37 (7): 70- 74
XIE Min, LOU Xin, LUO Xian Reviewed and developing trend of spacecraft fault diagnosis technology[J]. Software, 2016, 37 (7): 70- 74
doi: 10.3969/j.issn.1003-6970.2016.07.014
5 苏林, 尚朝轩, 刘文静 航天器姿态控制系统故障诊断方法概述[J]. 长春理工大学学报: 自然科学版, 2010, 33 (4): 23- 27
SU Lin, SHANG Chao-xuan, LIU Wen-jing Survey on the technology of fault diagnosis for spacecraft attitude control system[J]. Journal of Changchun University of Science and Technology: Natural Science Edition, 2010, 33 (4): 23- 27
6 PIRMORADI F N, SASSANI F, SILVA C W D Fault detection and diagnosis in a spacecraft attitude determination system[J]. Acta Astronautica, 2009, 65 (5): 710- 729
7 LE H X, MATUNAGA S A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites[J]. Acta Astronautica, 2014, 105 (1): 30- 39
doi: 10.1016/j.actaastro.2014.08.020
8 TUDOROIU N, SOBHANI-TEHRANI E, KHORASANI K. Interactive bank of unscented Kalman filters for fault detection and isolation in reaction wheel actuators of satellite attitude control system [C]// Conference of the IEEE Industrial Electronics Society. Taipei: IEEE, 2007: 264-269.
9 贾庆贤, 张迎春, 陈雪芹, 等 卫星姿态控制系统故障重构观测器设计[J]. 宇航学报, 2016, 37 (4): 442- 450
JIA Qing-xian, ZHANG Ying-chun, CHEN Xue-qin, et al Observer design for fault reconstruction in satellite attitude control system[J]. Journal of Astronautics, 2016, 37 (4): 442- 450
doi: 10.3873/j.issn.1000-1328.2016.04.010
10 吴丽娜, 张迎春 离散小波变换在卫星姿态控制系统故障中的应用[J]. 仪器仪表学报, 2006, 27 (6): 407- 409
WU Li-na, ZHANG Ying-chun Application of dispersing wavelet transform to fault diagnosis of the attitude control subsystem of the satellite[J]. Chinese Journal of Scientific Instrument, 2006, 27 (6): 407- 409
11 陈婷艳, 荆建平, 陈铁锋 经验模态分解方法在航天器故障诊断中的应用[J]. 噪声与振动控制, 2010, 30 (4): 76- 80
CHEN Ting-yan, JING Jian-ping, CHEN Tie-feng Application of empirical mode decomposition method in spacecraft fault diagnosis[J]. Noise and Vibration Control, 2010, 30 (4): 76- 80
doi: 10.3969/j.issn.1006-1355.2010.04.021
12 GUEDDI I, NASRI O, BENOTHMAN K, et al Fault detection and isolation of spacecraft thrusters using an extended principal component analysis to interval data[J]. International Journal of Control Automation and Systems, 2017, 15 (2): 1- 14
13 GUEDDI I, NASRI O, BENOTHMAN K, et al. VPCA-based fault diagnosis of spacecraft reaction wheels [C]// 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT). Kyoto: IEEE, 2015.
14 樊久铭, 宋政吉 分布式模糊专家系统设计及在卫星诊断中的应用[J]. 哈尔滨工业大学学报, 2005, 37 (5): 611- 613
FAN Jiu-ming, SONG Zheng-ji The design and application of distributed fuzzy expert diagnostic system for satellite[J]. Journal of Harbin Institute of Technology, 2005, 37 (5): 611- 613
doi: 10.3321/j.issn:0367-6234.2005.05.010
15 王展. 基于小波与BP神经网络的卫星速率积分陀螺故障诊断与隔离[D]. 武汉: 华中科技大学, 2008.
WANG Zhan. Fault diagnosis and isolation of satellite rate integral gyro based on wavelet and BP neural network [D]. Wuhan: Huazhong University of Science and Technology, 2008.
16 苏振华, 陆文高, 齐晶, 等 基于BP神经网络的卫星故障诊断方法[J]. 计算机测量与控制, 2016, 24 (5): 63- 66
SU Zhen-hua, LU Wen-gao, QI Jing, et al A method of satellite fault diagnosis based on BP neural network[J]. Computer Measurement and Control, 2016, 24 (5): 63- 66
17 BALDI P, BLANKE M, CASTALDI P, et al Combined geometric and neural network approach to generic fault diagnosis in satellite reaction wheels[J]. IFAC Papers Online, 2015, 48 (21): 194- 199
doi: 10.1016/j.ifacol.2015.09.527
18 程瑶. 卫星姿态控制系统的混合故障诊断方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
CHENG Yao. The combined approach for fault diagnosis of satellite attitude control system [D]. Harbin: Harbin Institute of Technology, 2016.
19 LIU G, ZHANG K, JIANG B. Adaptive observer-based fast fault estimation of a leader-follower linear multi-agent system with actuator faults [C]// Control Conference. Sabah: IEEE, 2015.
20 贺乃宝, 姜长生 基于Lyapunov方法的非线性系统自适应观测器设计[J]. 南京航空航天大学学报, 2006, 38 (3): 267- 270
HE Nai-bao, JIANG Chang-sheng Adaptive observer for nonlinear system based on Lyapunov approach[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2006, 38 (3): 267- 270
doi: 10.3969/j.issn.1005-2615.2006.03.001
21 FARAGHER R Understanding the basis of the Kalman filter via a simple and intuitive derivation[J]. IEEE Signal Processing Magazine, 2012, 29 (5): 128- 132
doi: 10.1109/MSP.2012.2203621
22 张德丰. MATLAB小波分析[M]. 2版. 北京: 机械工业出版社, 2012: 384.
23 孙延奎. 小波分析及其应用[M]. 北京: 机械工业出版社, 2005: 219.
24 URSZULA L, ZYGMUNT H. Wavelet based rule for fault detection [C]// 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes Safe Process. Warsaw: IFAC, 2018.
25 HAJIYEV C, CILDEN D, SOMOV Y Gyro-free attitude and rate estimation for a small satellite using SVD and EKF[J]. Aerospace Science and Technology, 2016, 55 (8): 324- 331
26 HAJIYEV C, CILDEN G D Review on gyroless attitude determination methods for small satellites[J]. Progress in Aerospace Sciences, 2017, 90 (4): 54- 66
[1] 张剑锋,赵朋,周宏伟,傅建中,陈子辰. 注射成形中聚合物熔体黏度的在线测量装置[J]. 浙江大学学报(工学版), 2020, 54(8): 1474-1480.
[2] 余鑫,金小军,莫仕明,张伟,徐兆斌,金仲和. 基于北斗B3频点的低轨卫星实时定轨性能评估[J]. 浙江大学学报(工学版), 2020, 54(3): 589-596.
[3] 张承志, 冯华君, 徐之海, 李奇, 陈跃庭. 图像噪声方差分段估计法[J]. 浙江大学学报(工学版), 2018, 52(9): 1804-1810.
[4] 陈学军, 杨永明. 采用经验小波变换的风力发电机振动信号消噪[J]. 浙江大学学报(工学版), 2018, 52(5): 988-995.
[5] 项贻强, 郏亚坤. 基于小波总能量相对变化的拱桥吊杆损伤识别[J]. 浙江大学学报(工学版), 2017, 51(5): 870-878.
[6] 苏星, 王慧泉, 金仲和. 实时高可靠综合电子系统的逻辑架构设计[J]. 浙江大学学报(工学版), 2017, 51(3): 628-636.
[7] 刘光辉, 周军, GUO Jian, 马学龙, 王成飞. 微型反作用飞轮速度估计与控制策略研究[J]. 浙江大学学报(工学版), 2017, 51(12): 2436-2443.
[8] 叶肖伟, 丁朋, 周诚, 李勇军, 倪一清, 董小鹏. 基于光纤传感技术的地铁隧道冻结法施工监测[J]. J4, 2013, 47(6): 1072-1080.
[9] 孔凡立,李霁,邹乐君,沈晓华,吴文渊,苏楠. 基于Haar小波变换的渐弃型废弃河道相识别[J]. J4, 2012, 46(3): 568-576.
[10] 杨将新, 程实, 曹衍龙, 郑华文, 何元峰, 谢永诚. 基于AR模型和小波变换的松动件定位方法[J]. J4, 2011, 45(8): 1366-1369.
[11] 佘青山, 孟明, 罗志增, 马玉良. 基于多核学习的下肢肌电信号动作识别[J]. J4, 2010, 44(7): 1292-1297.
[12] 胡志坤, 王美铃, 桂卫华, 阳春华, 丁家峰. 基于支持向量机的时序周波波形分类方法[J]. J4, 2010, 44(7): 1327-1332.
[13] 吴学成, 王怀, 浦世亮, 浦兴国, 袁镇福, 陈玲红, 岑可法. 数字共轴全息中颗粒识别与定位[J]. J4, 2010, 44(4): 765-770.
[14] 郑伟彦,吴为麟,吴剑强,王青岗. 基于SVR的电能质量数据压缩算法[J]. J4, 2010, 44(11): 2118-2123.
[15] 廖琬明 张玉贤 李东晓 张明. 基于小波变换的脆弱-鲁棒双重音频水印[J]. , 2009, 43(4): 721-726.