首页> 外文期刊>Advances in Acoustics and Vibration >Autocorrelation Analysis in Time and Frequency Domains for Passive Structural Diagnostics
【24h】

Autocorrelation Analysis in Time and Frequency Domains for Passive Structural Diagnostics

机译:时域和频域中的自相关分析,用于被动结构诊断

获取原文
获取外文期刊封面目录资料

摘要

In this paper, modal frequency estimation by using autocorrelation functions in both the time and frequency domains for structural diagnostics is discussed. With popular structural health monitoring methods for periodic inspections such as with the “hammering test,” hearing is very useful for distinguishing differences between structural conditions. Hearing detects pitch and tone, and it is known that the auditory process is related to wave periodicity calculated from autocorrelation functions. Consequently, on the basis of the hammering test, modal frequencies can be estimated by autocorrelation, the same as hearing. In this paper, modal frequencies were estimated by using autocorrelation for constant structural health monitoring under a nonstationary noise condition. First, fundamental modal frequencies were estimated by using the autocorrelation of the time domain which was inspired by pitch detection of hearing. Second, higher modal frequency compositions were also analyzed by using autocorrelation in the frequency domain as with tones discrimination. From the results by conducting scale-model experiments under unknown nonstationary noise conditions, periods of fundamental modal frequency were derived by using periods histogram of autocorrelation functions. In addition, higher modal frequency estimation under nonstationary noises was also discussed.
机译:本文讨论了在时域和频域中使用自相关函数进行模态频率估计的结构诊断方法。借助流行的用于定期检查的结构健康监测方法(例如“锤击测试”),听力对于区分结构条件之间的差异非常有用。听力检测到音调和音调,并且已知听觉过程与根据自相关函数计算出的波动周期有关。因此,基于锤击测试,模态频率可以通过自相关来估计,与听力相同。在本文中,模态频率通过使用自相关来估计,用于在非平稳噪声条件下进行恒定的结构健康监测。首先,通过使用时域的自相关来估计基本模态频率,该自相关是由听觉的音高检测启发的。其次,还通过使用频域中的自相关以及音调辨别来分析较高模态频率成分。通过在未知的非平稳噪声条件下进行比例模型实验的结果,使用自相关函数的周期直方图得出基本模态频率的周期。此外,还讨论了非平稳噪声下的更高模态频率估计。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号