基于CEEMDAN与SVD的泄流结构振动信号降噪方法

发布时间：2018-04-14 16:20

本文选题：泄流结构 + 集成经验模态分解　；参考：《振动与冲击》2017年22期

【摘要】：针对泄流结构振动信号非平稳性和特征信息被强噪声淹没的实际问题,提出一种基于具有自适应噪声的完整集成经验模态分解(CEEMDAN)和奇异值分解(SVD)联合的信号降噪方法。对一维泄流振动信号时程进行CEEMDAN分解,将信号分解为一系列固有模态函数分量(IMF),运用频谱分析方法筛选包含主要振动信息的IMF分量,滤除低频水流噪声,实现信号的初次滤波;利用排列熵理论确定含噪声较多的IMF分量,采用奇异值分解技术提取奇异值信息,运用奇异熵增量定阶理论滤除IMF分量中的高频噪声,实现信号的二次滤波;将包含结构振动信息的IMF分量重构,得到泄流结构的工作特征信息。结合拉西瓦模型振动实测数据,运用该方法进行计算分析,滤除高频和低频噪声,提取结构振动特征信息;结果表明该方法在泄流结构特征信息提取方面具有优越性,可为泄流结构在线监测和安全运行提供依据。
[Abstract]:In order to solve the practical problem that the vibration signal of discharge structure is nonstationary and that the characteristic information is submerged by strong noise, a signal denoising method based on the complete integrated empirical mode decomposition (CEEMDAN) with adaptive noise and singular value decomposition (SVD) is proposed.The time history of one dimensional discharge vibration signal is decomposed into a series of inherent mode function components by CEEMDAN decomposition. The IMF component containing the main vibration information is screened by the spectrum analysis method, and the low frequency flow noise is filtered to realize the first filtering of the signal.The permutation entropy theory is used to determine the IMF component with more noise, the singular value decomposition technique is used to extract the singular value information, and the singular entropy increment order theory is used to filter the high frequency noise in the IMF component to realize the quadratic filtering of the signal.The IMF component which contains the vibration information of the structure is reconstructed and the working characteristic information of the discharge structure is obtained.Combined with the measured data of Laxiwa model vibration, the method is used to calculate and analyze, filter the high frequency and low frequency noise, extract the structural vibration characteristic information, the result shows that this method has superiority in extracting the characteristic information of the discharge structure.It can provide basis for online monitoring and safe operation of discharge structure.
【作者单位】：华北水利水电大学水利学院;黄河水利职业技术学院土木与交通工程学院;
【基金】：国家自然科学基金(51679091;51408223) 河南省高校科技创新人才计划(18HASTIT012) 广东省水利科技创新基金(2017-16)
【分类号】：TV312

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