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首页> 外文会议>International Conference on Computer Science and Education >Vibration Fault Diagnosis Method for Planetary Gearbox of Wind Generating Set Based on EEMD
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Vibration Fault Diagnosis Method for Planetary Gearbox of Wind Generating Set Based on EEMD

机译：基于EEMD的风力发电机组行星齿轮箱振动故障诊断方法

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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.
机译：有关处理复杂条件下风力发电机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，采用整体经验模态分解法。 （EEMD）提取齿轮箱故障的特征，然后建立一个风力发电机组模拟测试台，以收集行星齿轮箱正常和故障情况下齿轮箱的振动信息，并通过EEMD，Envelope分解振动信号对有效的IMF分量进行频谱分析，并通过包络分析提取信号中的特征频率，并诊断行星齿轮箱的工作状态。行星齿轮箱正常和故障状态下振动数据的比较分析。结果表明，EEMD分解对振动信号的诊断和模态混叠的抑制具有非常明显的作用。它可以准确反映故障特征频率，验证了EEMD算法在行星齿轮箱故障诊断中的可行性。

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