经验模式分解的改进及其在水轮发电机组振动信号分析中的应用

经验模式分解可以将非线性、非平稳信号分解为有限个固有模式函数,在故障诊断中这些固有模式函数常常就是故障信号。但端点效应和分解终止条件的不当使其在分解过程中出现假频,限制了其应用。提出采用可变长极值镜像拓延法,对原信号两端包络进行拓延,有效地消除了端点效应;并提出在分解过程中采用不同的结束标准,使程序在适当的时候结束,提高了分解精度和速度。最后,将该方法应用于水轮发电机组振动信号分析中,取得了满意的效果。

Improving Empirical Mode Decomposition and Applying it to Analyzing Vibration Signals of a Hydroelectricity Generation Set

Empirical mode decomposition(EMD) can decompose nonlinear and non-stationary signals into a finite and often small number of intrinsic mode functions.The intrinsic mode functions are usually fault signals in fault diagnosis.However,their end effects and improper stop criteria result in non-existent frequencies during decomposition and constrain their application.To solve the problems,we use the alterable-length extremum mirroring extension algorithm to extend signal envelopes from both ends,thus effectively eliminating end effects.We also propose different stop criteria to stop the decompositon,raising EMD′s accuracy and speed.Finally we apply our methods to analyzing the vibration signals of a hydroelectricity generation set and achieve satisfactory results.