基于复数据的EMD在水导轴承故障诊断中的应用

Application of empirical mode decomposition based on complex data to fault diagnosis of hydraulic turbines

水轮机组是一个复杂的非线性动力系统,振动信号往往表现为非平稳性、非线性的特点,经验模态分解是一种新的时域分析方法,具有很好的适应性,较为适合处理非平稳性信号,但存在严重端点效应、模态混叠等问题。改进的集成经验模态分解一定程度上能够抑制模态混叠,但也会带来新的模态混叠、频谱丢失、运算量增大等问题。因此,本文将复数据经验模态分解运用到水轮机水导轴承的故障诊断中,添加白噪声作为虚部,从而构成复信号,通过白噪声在各个方向的投影来影响极值点的选取,同时利用噪声投影的影响再求包络质心的时候被消除的特性,从而抑制模态混叠。并通过水电站的实测信号验证该方法的有效性。

A hydraulic turbine unit is a complicated nonlinear dynamic system and its vibration signals are usually non-stationary and nonlinear. Empirical mode decomposition （EMD） is a new method for analysis of time domain signals; it has good adaptability and is more suitable for non-stationary signals, but it has serious problems with end effect, mode-mixing stack, and other adverse effects. The ensemble EMD （EEMD） reduces mode-mixing stack to a certain extent, but it improves little on new mode mixing and suffers from spectrum losing and higher computational cost. In this study, we have developed a complex data-based EMD （CEMD） and applied it to fault diagnosis of a turbine water guide bearing, adding white noise as the imaginary part to construct complex signals. This method adjusts the extremum points of a signal by projecting the white noise in all the directions, and it can reduce mode-mixing stack problem through eliminating the effect of noise projection in calculation of the envelope barycenter. Its application to the signals measured at a hydropower station verifies its validity and reliability.