期刊文献+

基于数据融合和LMD的厂房结构动参数识别研究 被引量:6

Dynamic parametric identification for a hydropower house based on data fusion and LMD
下载PDF
导出
摘要 针对水电站厂房振动监测中,振动测试易受到环境背景噪声和测点位置的影响,研究数据融合和局部均值分解(LMD)的组合方法,以提高厂房结构振动信号的信息的完整性和信噪比。将不同测点振动信号进行数据融合处理,提高信号信息完整性;融合信号经LMD分解为若干个PF(乘积函数)分量,通过频谱分析重构信号获得降噪信号;对降噪信号进行识别以获取有效动态参数。通过仿真信号分析,结果表明:该组合方法在振动信号动参数识别方面相对于单一的数字滤波、小波阈值和集合经验模式分解(EEMD)等方法具有一定的优势。将该方法应用于水电站厂房振动实测数据分析也取得了较好的结果。 The vibration monitoring accuracy of hydropower station structures is easy to be affected by environmental background noise and measured points' locations. In order to improve the signal-to-noise ratio of these vibration signals and their information integrity,the combined method based on data fusion and the local mean decomposition( LMD) was proposed. Firstly,vibration signals of different observation points were fused to improve the integrity of information. Then, the fused signals were decomposed with LMD into several product function( PF)components. Through the spectral analysis,the de-noised signals were reconstructed. Finally,the de-noised signals were identified to obtain effective dynamic parameters of the house structure. Through simulated signal analysis,it was shown that for dynamic parametric identification,the new method is superior to the digital filtering method,the wavelet threshold method and the ensemble empirical mode decomposition( EEMD). The proposed method was applied to analyze the actual measured vibration data of hydropower houses and better results were achieved.
出处 《振动与冲击》 EI CSCD 北大核心 2018年第2期175-181,共7页 Journal of Vibration and Shock
基金 国家重点研发计划课题(2016YFC0401902) 高等学校学科创新引智计划(B14012)
关键词 水电站厂房 振动信号 数据融合 局部均值分解 滤波降噪 hydropower house vibration signal data fusion local mean decomposition(LMD) noise reduction
  • 相关文献

参考文献9

二级参考文献79

共引文献410

同被引文献85

引证文献6

二级引证文献19

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