期刊文献+

用AR模型判断结构损伤的方法 被引量:4

A method of structural damage abnormally with AR model
原文传递
导出
摘要 利用AR模型的自回归系数构造距离判别函数、定义结构损伤特征参数,并引入统计分析方法,对结 构的异常状态诊断及损伤定位.数值实验表明,与基于模态频率变化的方法相比,该方法具有更高的损伤 识别能力. Using the AR model auto - regression coefficient to form function of distance - based identification, defining structural damaging diagnostic parameter and introducing statistic analytical method to identify abnormal structural state and lacate damage. Numeric experiments have shown that the method has a relative higher capacity of the damaging identification compare to those which bases on the variety of the model frequency.
出处 《福州大学学报(自然科学版)》 CAS CSCD 北大核心 2005年第z1期301-304,共4页 Journal of Fuzhou University(Natural Science Edition)
基金 福州大学科技发展基金资助项目(2004-XY-14)
关键词 AR模型 损伤特征参数 损伤诊断 AR model damaging diagnostic parameter damaging identification
  • 相关文献

参考文献4

  • 1[1]陈兆国.时间序列及其谱分析[M].北京:科学出版社,1986.
  • 2[2]Farra C R, Duffey T A, Doebling S W, et al . A statistical pattern recognition paradigm for vibration- based structural health monitoring[ A]. Proceedings of the 2nd International Workshop on Structural Health Monitoring[ C]. Stanford: [ s. n. ], 2000. 764- 773.
  • 3[3]Sohn H, Farra C R, Hunter N F. Structural health monitoring using statistical pattern recognition techniques[J]. Journal of Dynamic Systems, Measurement, and Control, 2001, 123: 706 - 711.
  • 4[4]Zhang Qi - wei. Statistical novelty detection for bridges using ambient vibration measurements[ A] . China- Japan Workshop on Vibration Control and Health Monitoring of Structures and the Third Chinese Symposium on Structural Vibration Control[ C]. Shanghai: [s.n.], 2002. 1-9.

同被引文献44

  • 1高品贤.趋势项对时域参数识别的影响及消除[J].振动.测试与诊断,1994,14(2):20-26. 被引量:28
  • 2GB/T4091-2001常规控制图[S].北京:中国标准出版社,2001.
  • 3欧进萍.重大工程结构的智能监测与健康诊断[C].崔京浩,编.第十一届结构工程学术会议论文集,第十一届全国结构工程学术会议,长沙.2002-10-20-23.北京:清华大学出版社,2002:44-53.
  • 4Doebling S W, Farrar C R, Prime M B. Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review[ R]. USA: Los Alamos National Laboratory ( No. LA- 13070-MS), 1996:5 - 62.
  • 5Sohn H, Farrar C R. Damage diagnosis using time series analysis of vibration signals [ J ]. Journal of Smart Materials and Structures,2001,10 ( 3 ) : 446 - 451.
  • 6Nair K K, Kiremidjian A S, Law K H. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structures [ J ]. Journal of Sound and Vibration, 2006, 291 (1/2) :349368.
  • 7Cheung A, Cabrera C, Sarabandi P, et al. The application of statistical pattern recogrfition methods for damage detection to field data[J]. Smart Materials and Structures,2008,12:1 -12.
  • 8Nair K K, Kiremidjian A S. Time series based structural damage detection algorithm using gaussian mixtures modeling [ J ]. Journal of Dynamic Systems, Measurement, and Control, 2007,129 ( 3 ) :258 - 293.
  • 9Sohn H, Farrar C R, Hunter H F,et al. Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat [ R ]. USA. Los Alamos National Laboratory (No. LA - 13761 - MS) ,2001.
  • 10Doebling S W, Farrar C R, Prime M B. A summary review of vibration-based damage identification methods [ J ]. Shock and Vibration Digest, 1998, 30(2): 91 -105.

引证文献4

二级引证文献51

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部