基于EEMD的风力发电机组行星齿轮箱振动故障诊断方法

Xianjiang Shi, Hongjian Li, Xiangdong Zhu, Yi Cao
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引用次数: 1

摘要

为了处理复杂工况下风电齿轮箱非平稳振动信号的预处理分析,本文以一阶行星齿轮箱为研究对象,采用集合经验模态分解(EEMD)提取齿轮箱故障特征。然后搭建风力发电机组仿真试验台,采集齿轮箱在行星齿轮箱正常和故障工况下的振动信息,对振动信号进行EEMD分解,对有效IMF分量进行包络谱分析,通过包络谱分析提取信号中的特征频率,诊断行星齿轮箱的工作状态。行星齿轮箱正常与故障状态振动数据的对比分析。结果表明,EEMD分解对振动信号的诊断和模态混叠的抑制效果非常明显。能够准确反映故障特征频率,验证了EEMD算法用于行星齿轮箱故障诊断的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vibration Fault Diagnosis Method for Planetary Gearbox of Wind Generating Set Based on EEMD
Detailed instructions can be found at In order to deal with the pre-processing analysis of non-stationary vibration signals of wind turbine gearbox under complex conditions, this paper takes the first-order planetary gearbox as the research object and uses the Ensemble Empirical Mode Decomposition (EEMD) to extract the feature of the faults in the gearbox, then build a wind generating set simulation test bench to collect the vibration information of the gearbox under the normal and fault conditions of the planetary gearbox and decompose the vibration signal by EEMD, Envelope spectrum analysis is performed on the effective IMF component, and the characteristic frequency in the signal is extracted by envelope analysis and the planetary gear box working state is diagnosed. Comparative analysis of vibration data in normal and faulty state of planetary gearboxes. It shows that EEMD decomposition has a very obvious effect on the diagnosing of vibration signals and the suppression of modal aliasing. It can accurately reflect the fault characteristic frequency and verify the feasibility of the EEMD algorithm for planetary gearbox fault diagnosis.
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