基于双模型自适应估计的大气数据传感器故障诊断方法

doi:10.12305/j.issn.1001-506X.2024.11.21

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (11): 3792-3799.doi: 10.12305/j.issn.1001-506X.2024.11.21

基于双模型自适应估计的大气数据传感器故障诊断方法

肖盈飞¹^,*, 刘海颖¹^,², 程月华³, 李铁香²^,⁴

1. 南京航空航天大学航天学院, 江苏南京 210016
2. 南京应用数学中心, 江苏南京 211135
3. 南京航空航天大学自动化学院, 江苏南京 210016
4. 东南大学数学学院, 江苏南京 211189

收稿日期:2023-05-17 出版日期:2024-10-28 发布日期:2024-11-30
通讯作者: 肖盈飞
作者简介:肖盈飞 (2000—), 女, 硕士研究生, 主要研究方向为传感器故障诊断、组合导航欺骗检测
刘海颖 (1980—), 男, 副教授, 博士, 主要研究方向为导航定位与测量、航天控制技术、无人智能系统、多源信息融合
程月华 (1977—), 女, 教授, 博士, 主要研究方向为集群故障协同诊断技术、系统健康状态监测与动态预测技术
李铁香 (1979—), 女, 教授, 博士, 主要研究方向为大规模矩阵计算及其应用、反问题计算、数值分析
基金资助:
中国航空科学基金(201908052002)

Fault diagnosis method of air data sensor based on double-model adaptive estimation

Yingfei XIAO¹^,*, Haiying LIU¹^,², Yuehua CHENG³, Tiexiang LI²^,⁴

1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Nanjing Center for Applied Mathematics, Nanjing 211135, China
3. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
4. School of Mathematics, Southeast University, Nanjing 211189, China

Received:2023-05-17 Online:2024-10-28 Published:2024-11-30
Contact: Yingfei XIAO

摘要/Abstract

摘要：

大气湍流是大气中一种不规则的随机运动, 在机载大气数据传感器的测量过程中, 大气湍流影响与传感器故障相互耦合, 导致大气数据传感器故障诊断算法无法将故障与湍流影响解耦。针对大气湍流影响下的大气数据传感器故障诊断问题, 基于惯性测量单元和导航姿态解算, 考虑湍流对大气系统的影响, 建立新型大气系统模型及量测模型。在对双模型自适应估计算法进行扩展后, 引入湍流对系统影响的协方差自适应更新方法, 实现存在未知扰动情况下故障状态的无偏估计。仿真结果表明, 该算法可以有效实现对固定偏差故障、漂移偏差故障和振荡故障的诊断。

关键词: 大气数据传感器故障诊断, 大气湍流, 双模型自适应估计, 故障检测与诊断

Abstract:

Atmospheric turbulence is an irregular random motion in the atmosphere. During the measurement of airborne air data sensors, atmospheric turbulence effects and sensor faults are coupled with each other, leading to the inability of air data sensor fault diagnosis algorithms to decouple faults from turbulence effects. In view of the fault diagnosis problem of air data sensors under the effects of atmospheric turbulence, a new atmospheric system model and measurement model are developed based on inertial measurement units and navigation attitude solving, considering the effects of turbulence on the atmospheric system. After extending the double-model adaptive estimation algorithm, the covariance adaptive update of the effect of turbulence on the system is introduced to achieve an unbiased estimation of the fault state in the presence of unknown disturbances. Simulation results show that the algorithm can effectively achieve diagnosis of fixed deviation faults, drift deviation faults, and oscillation faults.

Key words: air data sensor fault diagnosis, atmospheric turbulence, double-model adaptive estimation, fault detection and diagnosis

中图分类号:

V241.6

肖盈飞, 刘海颖, 程月华, 李铁香. 基于双模型自适应估计的大气数据传感器故障诊断方法[J]. 系统工程与电子技术, 2024, 46(11): 3792-3799.

Yingfei XIAO, Haiying LIU, Yuehua CHENG, Tiexiang LI. Fault diagnosis method of air data sensor based on double-model adaptive estimation[J]. Systems Engineering and Electronics, 2024, 46(11): 3792-3799.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 30

10	贾乾磊, 章卫国, 史静平, 等. FADS系统故障诊断方法研究[J]. 西北工业大学学报, 2020, 38 (6): 1210- 1217. doi: 10.3969/j.issn.1000-2758.2020.06.009
	JIA Q L , ZHANG W G , SHI J P , et al. Research on fault detection method of FADS system[J]. Journal of Northwestern Polytechnical University, 2020, 38 (6): 1210- 1217. doi: 10.3969/j.issn.1000-2758.2020.06.009
11	VENKATARAMAN R , BAUER P , SEILER P , et al. Comparison of fault detection and isolation methods for a small unmanned aircraft[J]. Control Engineering Practice, 2019, 84, 365- 376. doi: 10.1016/j.conengprac.2018.12.002
12	FREEMAN P , SEILER P , BALAS G J . Air data system fault modeling and detection[J]. Control Engineering Practice, 2013, 21 (10): 1290- 1301. doi: 10.1016/j.conengprac.2013.05.007
13	DE LOZA A F , CIESLAK J , HENRY D , et al. Sensor fault diagnosis using a non-homogeneous high-order sliding mode observer with application to a transport aircraft[J]. IET Control Theory & Applications, 2015, 9 (4): 598- 607.
14	HE Q Z , ZHANG W G , LU P , et al. Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis[J]. Aerospace Science and Technology, 2020, 98, 105649.
15	CHEN L J , PATTON R , GOUPIL P . Robust fault estimation using an LPV reference model: ADDSAFE benchmark case study[J]. Control Engineering Practice, 2016, 49, 194- 203.
16	梁天添, 王茂. 线性时变时滞连续-离散描述系统鲁棒故障诊断滤波器设计[J]. 中国惯性技术学报, 2018, (6): 841- 848.
	LIANG T T , WANG M . Robust fault diagnosis filer design for l inear sampled-data descriptor systems with time-varying delay[J]. Journal of Chinese Inertial Technology, 2018, (6): 841- 848.
17	SUN K, GEBRE-EGZIABHER D. A fault detection and isolation design for a dual pitot tube air data system[C]//Proc. of the IEEE/ION Position, Location and Navigation Symposium, 2020: 62-72.
18	CASTALDI P , MIMMO N , SIMANI S . Avionic air data sensors fault detection and isolation by means of singular perturbat ion and geometric approach[J]. Sensors, 2017, 17 (10): 2202.
19	SAMY I , POSTLETHWAITE I , GU D W , et al. Neural-network-based flush air data sensing system demonstrated on a mini air vehicle[J]. Journal of Aircraft, 2010, 47 (1): 18- 31.
20	TAIMOOR M , AIJUN L , SAMIUDDIN M . Sliding mode learning algorithm based adaptive neural observer strategy for fault estimation, detection and neural controller of an aircraft[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12 (2): 2547- 2571.
21	DONG Y Q, WEN J R, ZHANG Y M, et al. Deep neural networks-based air data sensors fault detection for aircraft[C]//Proc. of the 33rd Chinese Control and Decision Conference, 2021: 442-447.
22	ZHAO Y M , ZHAO H , AI J L , et al. Robust data-driven fault detection: an application to aircraft air data sensors[J]. Interna tional Journal of Aerospace Engineering, 2022, 2022, 2918458.
23	MONTAZERI A , KARGAR S M . Fault detection and diagnosis in air handling using data-driven methods[J]. Journal of Building Engineering, 2020, 31, 101388.
24	LU P , VAN KAMPEN E J , DE VISSER C , et al. Air data sensor fault detection and diagnosis in the presence of atmospheric turbulence: theory and experimental validation with real flight data[J]. IEEE Trans.on Control Systems Technology, 2020, 29 (5): 2255- 2263.
25	VAN EYKEREN L, CHU Q. Air data sensor fault detection using kinematic relations[C]//Proc. of the Advances in Aerospace Guidance, Navigation and Control: Selected Papers of the Second CEAS Specialist Conference on Guidance, Navigation and Control, 2013: 183-197.
26	赵磊. 导弹气动/大气参数一体化在线辨识与预示方法研究[D]. 长沙: 国防科技大学, 2018.
	ZHAO L. Research on integrated on-line identification and pre diction method of missile aerodynamic/atmospheric parameters[D]. Changsha: National University of Defense Technology, 2018.
27	LU P , VAN EYKEREN L , VAN KAMPEN E , et al. Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis[J]. Journal of Guidance, Control, and Dynamics, 2015, 38 (8): 1409- 1424.
28	赵仁杰, 胡柏青, 吕旭, 等. 基于双欧拉角的UKF组合导航滤波算法[J]. 系统工程与电子技术, 2021, 43 (7): 1912- 1920.
1	赵洁珏. 民用飞机典型大气数据系统故障分析[J]. 科技视界, 2018, 241 (19): 30- 31.
	ZHAO J J . Typical air data system fault analysis of civilian air craft[J]. Science & Technology Vision, 2018, 241 (19): 30- 31.
2	崔小丹, 程咏梅, 李振威. 多余度大气/惯性系统高灵敏度故障检测方法[J]. 西北工业大学学报, 2020, 38 (5): 971- 976. doi: 10.3969/j.issn.1000-2758.2020.05.008
	CUI X D , CHENG Y M , LI Z W . Redundant ADS and INS high sensitivity fault detection method[J]. Journal of Northwestern Polytechnical University, 2020, 38 (5): 971- 976. doi: 10.3969/j.issn.1000-2758.2020.05.008
3	LI Z W , CHENG Y M , WANG H B , et al. Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time-offset[J]. IET Radar, Sonar & Navigation, 2021, 15 (9): 945- 956.
4	PALNER C . The boeing 737 max saga: automating failure[J]. Engineering, 2020, 6 (1): 2- 3. doi: 10.1016/j.eng.2019.11.002
5	KILIC U , UNAL G . Aircraft air data system fault detection and reconstruction scheme design[J]. Aircraft Engineering and Ae-rospace Technology, 2021, 93 (6): 1104- 1114. doi: 10.1108/AEAT-01-2021-0018
6	宋德龙. 基于多普勒激光雷达的湍流预警研究[D]. 天津: 中国民航大学, 2021.
	SONG D L. Research on early warning of turbulence based on Doppler lidar[D]. Tianjing: Civil Aviation University of China, 2021.
7	丁智坚, 周欢, 吴东升, 等. 嵌入式大气数据测量系统技术研究进展[J]. 宇航学报, 2019, 40 (3): 247- 257.
	DING Z J , ZHOU H , WU D S , et al. Review of flush air data sensing system[J]. Journal of Astronautics, 2019, 40 (3): 247- 257.
8	孟博, 易华. 大气数据对空空导弹武器系统的影响研究[J]. 电光与控制, 2013, 20 (4): 87- 91.
	MENG B , YI H . Effects of air data on air-to-air missile weapon system[J]. Electronics Optics & Control, 2013, 20 (4): 87- 91.
9	蒋红娜, 姜宏伟, 白雪. 机载光学大气数据系统研究与应用[J]. 国外电子测量技术, 2021, 40 (5): 146- 149.
	JIANG H N , JIANG H W , BAI X . Research and application of airborne optical atmospheric data system[J]. Foreign Electronic Measurement Technology, 2021, 40 (5): 146- 149.
28	ZHAO R J , HU B Q , LYU X , et al. Filtering algorithm of UKF integrated navigation based on dual-Euler angles[J]. Systems Engineering and Electronics, 2021, 43 (7): 1912- 1920.
29	LI K Y , GUO Z K , ZHOU G J . State estimation in range coordinate using range-only measurements[J]. Journal of Systems Engineering and Electronics, 2022, 33 (3): 497- 510.
30	YANG Z B , CHENG B , LV C X , et al. Fuzzy neural network dynamic inverse control strategy for quadrotor UAV based on at mospheric turbulence[J]. Applied Sciences, 2022, 12 (23): 12232.

[1]	戴洪德, 马宇峰, 戴邵武, 郑百东, 张笑宇. 航向误差非线性预测的惯性行人导航零速修正算法[J]. 系统工程与电子技术, 2023, 45(8): 2555-2561.
[2]	郭鹏飞, 任章, 邱海韬, 杨云春. 机动加速度辅助的航姿系统扩展卡尔曼滤波[J]. Journal of Systems Engineering and Electronics, 2009, 31(3): 625-628,637.

基于双模型自适应估计的大气数据传感器故障诊断方法

Fault diagnosis method of air data sensor based on double-model adaptive estimation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 30

相关文章 2

编辑推荐

Metrics

本文评价