EMD密集模态识别研究及在电站厂房中的应用

发布时间：2018-03-13 13:59

本文选题：经验模态分解　切入点：密集模态　出处：《水力发电学报》2017年06期 　论文类型：期刊论文

【摘要】：针对传统经验模态分解(EMD)密集模态分解能力不足的问题,提出信号调频预处理和优化模态筛选准则相结合的改进方法。研究了模态筛选准则、迭代次数、信号调频与EMD模态分解能力的关系,并给出了信号调频的具体过程;提出基于窗函数、信号调频和改进EMD模态筛选准则的密集模态识别方法,并分别通过仿真信号和电站厂房实测振动信号的模态参数识别进行验证。试验成果表明,改进方法能够有效识别密集模态参数,为EMD应用于电站厂房等密集模态结构参数识别奠定了基础。
[Abstract]:In order to solve the problem that the traditional empirical mode decomposition (EMD) has insufficient capacity of dense mode decomposition, an improved method combining signal frequency modulation preprocessing with optimal modal screening criterion is proposed, and the modal screening criterion and iteration times are studied. The relationship between signal frequency modulation and mode decomposition ability of EMD is discussed, and the specific process of signal frequency modulation is given, and a method of dense modal identification based on window function, signal frequency modulation and improved EMD modal screening criterion is proposed. The experimental results show that the improved method can identify the dense modal parameters effectively. It lays a foundation for the application of EMD to the identification of structural parameters of dense modes such as power plant.
【作者单位】：天津大学水利工程仿真与安全国家重点实验室;天津大学建筑工程学院;青海大学水利电力学院;
【基金】：国家重点研发计划(2016YFC0401902) 国家自然科学基金(51579172)
【分类号】：TN911.7;TV731

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