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。
In view of the shortcoming of traditional empirical mode decomposition （EMD）, the method is improved by using signal frequency modulation and optimizing mode selection criterion. The relationship of decomposition ability versus selection criterion, iteration number, and frequency modulation was studied, and a detailed procedure of frequency modulation is given. A closely-spaced mode identification method was developed using window functions, signal frequency modulation, and improved selection criterion, and it was validated via simulations of vibration signals observed in-situ. The results show that the improved method can effectively identify the parameters of closely-spaced modes, thus laying a basis for EMD application to power plant analysis and identification of its vibration parameters.
出处
《水力发电学报》
EI
CSCD
北大核心
2017年第6期103-113,共11页
Journal of Hydroelectric Engineering
基金
国家重点研发计划(2016YFC0401902)
国家自然科学基金(51579172)
关键词
经验模态分解
密集模态
参数识别
调频
筛选准则
empirical mode decomposition
closely spaced modes
parameter identification
frequency modulation
selection criterion
