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框架结构非线性损伤的主成分分析识别方法研究

赵仕通 肖黎 屈文忠

赵仕通, 肖黎, 屈文忠. 框架结构非线性损伤的主成分分析识别方法研究[J]. 机械科学与技术, 2018, 37(1): 8-12. doi: 10.13433/j.cnki.1003-8728.2018.0102
引用本文: 赵仕通, 肖黎, 屈文忠. 框架结构非线性损伤的主成分分析识别方法研究[J]. 机械科学与技术, 2018, 37(1): 8-12. doi: 10.13433/j.cnki.1003-8728.2018.0102
Zhao Shitong, Xiao li, Qu Wenzhong. Study on Nonlinear Damage Identification of Frame Structure with a Principal Component Analysis Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 8-12. doi: 10.13433/j.cnki.1003-8728.2018.0102
Citation: Zhao Shitong, Xiao li, Qu Wenzhong. Study on Nonlinear Damage Identification of Frame Structure with a Principal Component Analysis Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 8-12. doi: 10.13433/j.cnki.1003-8728.2018.0102

框架结构非线性损伤的主成分分析识别方法研究

doi: 10.13433/j.cnki.1003-8728.2018.0102
基金项目: 

国家自然科学基金项目(51378402)资助

详细信息
    作者简介:

    赵仕通(1993-),硕士研究生,研究方向为结构健康监测,zhaoshitong@whu.edu.cn

    通讯作者:

    屈文忠,教授,博士生导师,qwz@whu.edu.cn

Study on Nonlinear Damage Identification of Frame Structure with a Principal Component Analysis Method

  • 摘要: 针对框架结构非线性损伤识别问题,提出一种基于主成分分析的损伤识别方法。利用主成分分析数据压缩和特征提取的特性,首先对结构基准工况响应信号进行处理,提取特征成分,得到主成分模型,然后将结构未知工况响应数据向主成分模型投影,通过构造损伤指标实现对结构非线性损伤的识别。以四层钢框架结构碰撞模型为实验对象,通过螺栓和钢柱构造碰撞非线性损伤源,实验结果表明该方法可有效识别结构的非线性损伤。
  • [1] 袁慎芳.结构健康监控[M].北京:国防工业出版社,2007 Yuan S F. Structural health monitoring and damage control[M]. Beijing:National Defence Industry Press, 2007(in Chinese)
    [2] Farrar C R, Worden K. An introduction to structural health monitoring[J]. Philosophical Transactions of the Royal Society A:Mathematical, Physical and Engineering Sciences, 2007,365(1861):303-315
    [3] 朱军华,余岭.结构损伤响应时程主成分及其相关性分析[J].振动与冲击,2012,31(19):156-159 Zhu J H, Yu L. Correlation analysis for principal components of damage response time histories of a structure[J]. Journal of Vibration and Shock, 2012,31(19):156-159(in Chinese)
    [4] Sohn H, Farrar C R, Hemez F M, et al. A review of structural health monitoring literature:1996-2001[R]. LA-13976-MS. New Mexico:Los Alamos National Laboratory, 2004
    [5] 朱宏平,余璟,张俊兵.结构损伤动力检测与健康监测研究现状与展望[J].工程力学,2011,28(2):1-11,17 Zhu H P, Yu J, Zhang J B. A summary review and advantages of vibration based damage identification methods in structural health monitoring[J]. Engineering Mechanics, 2011,28(2):1-11,17(in Chinese)
    [6] Mujica L E, Vehi J, Ruiz M, et al. Multivariate statistics process control for dimensionality reduction in structural assessment[J]. Mechanical Systems and Signal Processing, 2008,22(1):155-171
    [7] Bandara R P, Chan T H T, Thambiratnam D P. Structural damage detection method using frequency response functions[J]. Structural Health Monitoring, 2014,13(4):418-429
    [8] Mujica L, Rodellar A, Fernandez A, et al. Q-statistic and T2-statistic PCA-based measures for damage assessment in structures[J]. Structural Health Monitoring, 2011,10(5):539-553
    [9] Yan A M, Kerschen G, De Boe P, et al. Structural damage diagnosis under varying environmental conditions-Part I:a linear analysis[J]. Mechanical Systems and Signal Processing, 2005,19(4):847-864
    [10] 于秀林,任雪松.应用多元统计分析[M].2版.北京:中国统计出版社,2011 Yu X L, Ren X S. Applied multivariate statistical analysis[M]. 2nd ed. Beijing:China Statistics Press, 2011(in Chinese)
    [11] Sierra-Pérez J, Guemes A, Mujica L E, et al. Damage detection in composite materials structures under variable loads conditions by using fiber Bragg gratings and principal component analysis, involving new unfolding and scaling methods[J]. Journal of Intelligent Material Systems and Structures, 2015,26(11):1346-1359
    [12] Manson G, Worden K, Holford K, et al. Visualisation and dimension reduction of acoustic emission data for damage detection[J]. Journal of Intelligent Material Systems and Structures, 2001,12(8):529-536
    [13] Tibaduiza D A, Mujica L E, Rodellar J, et al. Structural damage detection using principal component analysis and damage indices[J]. Journal of Intelligent Material Systems and Structures, 2016,27(2):233-248
    [14] Gharibnezhad F, Mujica L E, Rodellar J. Applying robust variant of principal component analysis as a damage detector in the presence of outliers[J]. Mechanical Systems and Signal Processing, 2015,50-51:467-479
    [15] Tibaduiza D A, Mujica L E, Rodellar J. Damage classification in structural health monitoring using principal component analysis and self-organizing maps[J]. Structural Control & Health Monitoring, 2013,20(10):1303-1316
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出版历程
  • 收稿日期:  2016-10-19
  • 刊出日期:  2018-01-15

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