电子电气工程与控制

基于相关证据推理规则的激光惯组健康评估

  • 唐帅文 ,
  • 曹友 ,
  • 张朋 ,
  • 姜江
展开
  • 1.国防科技大学 系统工程学院,长沙 410073
    2.火箭军士官学校 控制技术教研室,青州 262500
    3.西安卫星测控中心 宇航动力学国家重点实验室,西安 710043
.E-mail: 936756268@qq.com

收稿日期: 2023-08-16

  修回日期: 2023-08-30

  录用日期: 2023-09-08

  网络出版日期: 2023-09-20

基金资助

国家自然科学基金(62303474);山东省自然科学基金(ZR2023QF010)

Health assessment of LIMU based on evidential reasoning rule with dependent evidence

  • Shuaiwen TANG ,
  • You CAO ,
  • Peng ZHANG ,
  • Jiang JIANG
Expand
  • 1.College of Systems Engineering,National University of Defense Technology,Changsha  410073,China
    2.Control Technology Teaching and Research Office,Rocket Army Sergeant School,Qingzhou  262500,China
    3.State Key Laboratory of Aerospace Dynamics,Xi’an Satellite Control Center,Xi’an  710043,China
E-mail: 936756268@qq.com

Received date: 2023-08-16

  Revised date: 2023-08-30

  Accepted date: 2023-09-08

  Online published: 2023-09-20

Supported by

National Natural Science Foundation of China(62303474);Natural Science Foundation of Shandong Province(ZR2023QF010)

摘要

激光惯组(LIMU)是复杂装备系统的重要导航单机,其良好的健康状态对于确保系统安全稳定工作具有重要意义。针对激光惯组健康评估中面临的评估指标体系不健全、评估框架不一致、评估指标不独立和评估模型参数不确定问题,在激光惯组工作机理分析的基础上,构建健康评估指标体系,提出基于相关证据推理规则(ERR-DE)的激光惯组健康评估方法。首先,提出转换矩阵的概念,实现了不同评估框架的统一转换。其次,采用变异系数法确定证据权重,采用基于距离的方法确定证据可靠度。然后,基于证据可靠度提出证据融合顺序确定机制,提出基于相对总体相关系数(RTDC)的证据相关性分析方法,并在证据推理规则(ERR)的基础上提出了相关证据推理规则。再者,构建参数优化模型以确定最优评估参数,提高评估精度,进一步对评估算法进行了总结。最后,通过某型激光惯组健康评估案例和对比研究,验证了所提方法的有效性。

本文引用格式

唐帅文 , 曹友 , 张朋 , 姜江 . 基于相关证据推理规则的激光惯组健康评估[J]. 航空学报, 2024 , 45(12) : 329453 -329453 . DOI: 10.7527/S1000-6893.2023.29453

Abstract

The Laser Inertial Measurement Unit (LIMU) is an important navigation unit for the complex equipment system, and its good health state is of great significance for ensuring safe and stable operation of the system. In the health assessment of LIMU, there exist the problems of imperfect assessment indicator system, inconsistent assessment framework, dependent assessment indicators and uncertain parameters of assessment model. Based on the analysis of working mechanism of LIMU, a health assessment indicator system is constructed, and a health assessment method of LIMU is proposed based on the Evidential Reasoning Rule with Dependent Evidence (ERR-DE). Firstly, the concept of transformation matrix is proposed to achieve unified transformation of different assessment frameworks. Secondly, the coefficient of the variation-based weighting method is used to determine evidence weights, and the distance-based method is employed to determine evidence reliabilities. Then, based on the evidence reliability, a mechanism to determine the aggregation sequence of evidences is proposed, and an evidence dependence analysis method is proposed based on the Relative Total Dependence Coefficient (RTDC). Based on the Evidential Reasoning Rule (ERR), the ERR-DE is proposed. Furthermore, a parameter optimization model is constructed to determine the optimal assessment parameters and improve the assessment accuracy. The assessment algorithm is further summarized. Finally, the effectiveness of the proposed method is verified through a health assessment case study and comparative studies on a certain type of LIMU.

参考文献

1 ZHOU Z J, NING P Y, WANG J, et al. An evidential reasoning rule-based quality state assessment method of complex systems considering feature selection[J]. IEEE Transactions on Instrumentation and Measurement202372: 2510513.
2 CHEN L Y, ZHOU Z J, HU C H, et al. Performance evaluation of complex systems using evidential reasoning approach with uncertain parameters[J]. Chinese Journal of Aeronautics202134(1): 194-208.
3 ZHURAVLEV V P, PERELVAEV S E, BODUNOV B P, et al. New-generation small-size solid-state wave gyroscope for strapdown inertial navigation systems of unmanned aerial vehicle[C]∥2019 26th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS). Piscataway: IEEE Press, 2019: 1-3.
4 邵世纲, 杨泽萱, 邢冠楠, 等. 国外主要战略导弹系列化发展及技术共用研究[J]. 宇航总体技术20171(3): 24-32.
  SHAO S G, YANG Z X, XING G N, et al. Research on serialization and technology sharing of foreign major strategic missiles[J]. Astronautical Systems Engineering Technology20171(3): 24-32 (in Chinese).
5 谢冉. 复杂系统建模方法综述[J]. 现代防御技术202048(3): 31-36, 68.
  XIE R. Survey of complex system modeling methods[J]. Modern Defence Technology202048(3): 31-36, 68 (in Chinese).
6 李永满. 某型激光陀螺捷联惯组测试系统设计[D]. 哈尔滨: 哈尔滨工业大学, 2013.
  LI Y M. The test system design on the measurement unit of laser gyro strapdown inertial[D]. Harbin: Harbin Institute of Technology, 2013 (in Chinese).
7 董春梅, 任顺清, 陈希军, 等. 激光陀螺捷联惯导系统的模观测标定方法[J]. 红外与激光工程201847(9): 0917007.
  DONG C M, REN S Q, CHEN X J, et al. Calibration method for the laser gyro strapdown inertial navigation system based on norm-observation[J]. Infrared and Laser Engineering201847(9): 0917007 (in Chinese).
8 王堃, 徐兵华, 孟凡军. 激光捷联惯组失效模式分析及性能评估方法研究[J]. 航空精密制造技术202056(6): 11-14.
  WANG K, XU B H, MENG F J. Failure mode analysis and performance evaluation of laser strapdown inertial unit[J]. Aviation Precision Manufacturing Technology202056(6): 11-14 (in Chinese).
9 王易南, 闫杰. 基于卫星和星敏感器的冗余激光惯组在轨标定[J]. 飞行器测控学报201635(5): 358-364.
  WANG Y N, YAN J. On-orbit calibration method for redundant inertial measurement units by satellite and star sensors[J]. Journal of Spacecraft TT&C Technology201635(5): 358-364 (in Chinese).
10 王子超, 范会迎, 谢元平, 等. 捷联惯导系统复杂误差参数系统级标定方法[J]. 红外与激光工程202251(7): 3788/IRLA20210499.
  WANG Z C, FAN H Y, XIE Y P, et al. System-level calibration method for complex error coefficients of strapdown inertial navigation system[J]. Infrared and Laser Engineering202251(7): 3788/IRLA20210499 (in Chinese).
11 罗睿, 李鹏, 于玲燕. 一种考虑加表不对称误差的冗余惯组标定方法[J]. 中国惯性技术学报202331(2): 114-120.
  LUO R, LI P, YU L Y. A calibration method for redundant IMU considering accelerometer asymmetric error[J]. Journal of Chinese Inertial Technology202331(2): 114-120 (in Chinese).
12 吴克雄, 王振华. 基于专家系统和数据驱动的健康评估分析方法[J]. 黑龙江科学201910(16): 64-65.
  WU K X, WANG Z H. Health assessment and analysis method based on expert system and data driven[J]. Heilongjiang Science201910(16): 64-65 (in Chinese).
13 陈雷雨, 周志杰, 唐帅文, 等. 融合多元信息的武器装备性能评估方法[J]. 系统工程与电子技术202042(7): 1527-1533.
  CHEN L Y, ZHOU Z J, TANG S W, et al. Weapon equipment performance evaluation method with multi-information fusion[J]. Systems Engineering and Electronics202042(7): 1527-1533 (in Chinese).
14 WANG J, ZHOU Z J, HU C H, et al. An evidential reasoning rule considering parameter uncertainty[J]. IEEE Transactions on Aerospace and Electronic Systems202258(2): 1391-1404.
15 YANG J B, XU D L. Evidential reasoning rule for evidence combination[J]. Artificial Intelligence2013205: 1-29.
16 周志杰, 唐帅文, 胡昌华, 等. 证据推理理论及其应用[J]. 自动化学报202147(5): 970-984.
  ZHOU Z J, TANG S W, HU C H, et al. Evidential reasoning theory and its applications[J]. Acta Automatica Sinica202147(5): 970-984 (in Chinese).
17 ZHOU M, LIU X B, CHEN Y W, et al. Evidential reasoning rule for MADM with both weights and reliabilities in group decision making[J]. Knowledge-Based Systems2018143: 142-161.
18 KONG G L, XU D L, YANG J B, et al. Evidential reasoning rule-based decision support system for predicting ICU admission and in-hospital death of trauma[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems202151(11): 7131-7142.
19 ZHANG B C, ZHANG A X, HU G Y, et al. Reliability assessment of train control and management system based on evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm[J]. ISA Transactions2021116: 129-138.
20 TANG S W, ZHOU Z J, HU C H, et al. A new evidential reasoning rule-based safety assessment method with sensor reliability for complex systems[J]. IEEE Transactions on Cybernetics202252(5): 4027-4038.
21 YANG J B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties[J]. European Journal of Operational Research2001131(1): 31-61.
22 唐帅文, 周志杰, 姜江, 等. 考虑扰动的无人机集群协同态势感知一致性评估[J]. 航空学报202041(S2): 724233.
  TANG S W, ZHOU Z J, JIANG J, et al. Consistency assessment of cooperative situation awareness of UAV cluster considering disturbance[J]. Acta Aeronautica et Astronautica Sinica202041(S2): 724233 (in Chinese).
23 LIU P D, LI Y, ZHANG X H, et al. A multiattribute group decision-making method with probabilistic linguistic information based on an adaptive consensus reaching model and evidential reasoning[J]. IEEE Transactions on Cybernetics202353(3): 1905-1919.
24 ZHAO F J, ZHOU Z J, HU C H, et al. A new evidential reasoning-based method for online safety assessment of complex systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems201848(6): 954-966.
25 DENG J X, DENG Y, CHEONG K H. Combining conflicting evidence based on Pearson correlation coefficient and weighted graph[J]. International Journal of Intelligent Systems202136(12): 7443-7460.
26 YAO S Y, YANG J B, XU D L, et al. Probabilistic modeling approach for interpretable inference and prediction with data for sepsis diagnosis[J]. Expert Systems with Applications2021183: 115333.
27 ZHOU J, SU X Y, QIAN H. Research on fusion of dependent evidence based on Kendall correlation coefficient[C]∥Proceedings of the 4th International Conference on Computer Science and Application Engineering. New York: ACM, 2020: 1-5.
28 JIANG W. A correlation coefficient for belief functions[J]. International Journal of Approximate Reasoning2018103: 94-106.
29 YAGER R R. On the fusion of non-independent belief structures[J]. International Journal of General Systems200938(5): 505-531.
30 ZHANG P, ZHOU Z J, TANG S W, et al. On the evidential reasoning rule for dependent evidence combination[J]. Chinese Journal of Aeronautics202336(5): 306-327.
31 SZéKELY G J, RIZZO M L, BAKIROV N K. Measuring and testing dependence by correlation of distances[J]. The Annals of Statistics200735(6): 2769-2794.
文章导航

/