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针对风电机组滚动轴承工作环境恶劣、工况多变且振动信号成分复杂等特点， 将33项时域和频域特征参数及其特性应用于风电机组滚动轴承状态监测和故障诊断中， 利用奇异值分解重构法（Singular Value Decomposition， SVD）将滚动轴承振动故障信号中的噪声等干扰成分去除， 降噪重构后的信号经过基于经验模式分解法（Empirical Mode Decomposition， EMD）的希尔伯特-黄变换， 实现故障冲击信号的共振解调处理， 将低频周期故障调制信号筛选出来， 最终结合滚动轴承各部件故障特征频率、振动信号时频分析结果和时频特征参数诊断结果实现滚动轴承的状态监测和故障识别。 并通过振动测试信号分析， 验证了该方法对提取风电机组滚动轴承故障特征的有效性。
Abstract：Aiming at the characteristics of wind turbine rolling bearings, such as harsh working environment, variable working conditions and complex vibration signal components, the author applies 33 time and frequency domain characteristic parameters and their properties to the rolling bearing condition monitoring and fault diagnosis of wind turbine, and deconstructs the rolling bearing vibration fault signal by using Singular Value Decomposition(SVD) to remove noise and other interfering components. The noise and other interference components are removed by SVD, and the signal after noise reduction and reconstruction undergoes the Hilbert-Yellow transformation based on Empirical Mode Decomposition(EMD) to realize the resonance demodulation of the faulty impact signal, and the low-frequency fault modulation signal is screened out, and finally, the combination of the characteristic frequency of the faults of each component of the rolling bearing, the timefrequency analysis of the vibration signal and the diagnostic results of the time-frequency characteristic parameters are combined. Finally, combined with the fault characteristic frequency of rolling bearing components, vibration signal time - frequency analysis and time-frequency characteristic parameter diagnosis results to realize the condition monitoring and fault identification of rolling bearings. And the vibration test signal verification analysis verifies the effectiveness of the method to extract the rolling bearing fault characteristics of wind turbine.
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基本信息:
DOI：10.19929/j.cnki.nmgdljs.2024.0035
中图分类号:
引用信息:
[1]张利慧,李殊瑶,李晓波等.基于时频域特征参数的风电机组滚动轴承故障诊断方法及应用[J].内蒙古电力技术,2024,42(03):13-19.DOI:10.19929/j.cnki.nmgdljs.2024.0035.
基金信息:
内蒙古电力（集团）有限责任公司内蒙古电力科学研究院分公司青年科技人员支持计划项目“风力发电机组滚动轴承振动特征分析与故障特征提取方法优化”（2021-QK-01）