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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2022, Vol. 39 ›› Issue (1): 37-49.doi: 10.3969/j.issn.1005-3085.2022.01.003

• • 上一篇    下一篇

面向航天器结构健康状态监测的数据压缩感知算法研究

李  钰1,2,   李  晨3,  王常龙3,   梅占东4,   张世一1,2   

  1. 1. 上海卫星装备研究所，上海  200240
    2. 上海空间环境模拟与验证工程技术研究中心，上海 200240
    3. 西安电子科技大学电子信息攻防对抗与仿真技术教育部重点实验室，西安 710071
    4. 西安交通大学数学与统计学院，西安 710049
  • 出版日期:2022-02-15 发布日期:2022-04-15
  • 通讯作者: 李 晨 E-mail: chen195@foxmail.com

Research on Data Compressed Sensing Algorithm for Spacecraft Structural Health Monitoring

LI Yu1,2,  LI Chen3,   WANG Changlong3,   MEI Zhandong4,   ZHANG Shiyi1,2   

  1. 1. Shanghai Institute of Satellite Equipment, Shanghai 200240 
    2. Shanghai Space Environment Simulation and Verification Engineering Technology Research Center, Shanghai 200240 
    3. Key of Laboratory of Electronic Information Countermeasure and Simulation Technology of the Education Ministry, Xidian University, Xi'an 710071 
    4. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049
  • Online:2022-02-15 Published:2022-04-15
  • Contact: C. Li. E-mail address: chen195@foxmail.com

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摘要：

航天器产品结构健康状态监测是保证其发射与在轨运行过程中安全平稳运行的重要环节，由于多个传感器在长时间监测过程中会产生大量数据并需要对其进行高效传输和存储，本文针对数据传输问题提出并证明了基于稀疏恢复技术的一种分式最小化模型算法和改进的分式筛选算法，并结合卫星振动试验数据与经典匹配追踪等算法进行了对比分析验证，对比结果表明，在航天器所处环境条件下，所提算法处理得到的数据恢复相对误差均低于两种现有算法，可以在较高压缩比的条件下保证数据恢复的精度，实现了低维数据到高维数据传输。

关键词: 压缩感知, 结构健康监测, 振动试验, 稀疏恢复

Abstract:

The structural health monitoring of a spacecraft product is an important process to ensure its safe and stable operation during launch and in orbit. Since multiple sensors will generate a large amount of data during long-term monitoring and require efficient transmission and storage, this article is aimed at data transmission problem puts forward and proves a fractional minimization model algorithm based on sparse recovery technology and an improved fractional screening algorithm, combined with satellite vibration test data and classic matching tracking algorithms for comparative analysis and verification. The comparison results show that, under the environmental conditions of the spacecraft, the relative error of data recovery obtained by the algorithm in this paper is lower than that of the two existing algorithms. The accuracy of data recovery is guaranteed under the condition of higher compression ratio, and hence we implement the data recovery from low-dimensional data to high-dimensional data transmission.

Key words: compressed sensing, structural health monitoring, vibration testing, sparse recovery

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