基于VMD与自相关分析的滚动轴承故障特征提取

发布时间：2018-06-18 09:09

本文选题：自相关分析 + 变分模态分解　；参考：《电子测量与仪器学报》2017年09期

【摘要】：滚动轴承故障信号多呈现非平稳、多分量调制特性,早期故障信号调制特性微弱、易受周围设备噪声干扰,导致轴承早期故障特征淹没在噪声信号中,故障特征难以提取。为此,提出一种变分模态分解(variational mode decomposition,VMD)与自相关分析相结合的轴承故障特征提取方法。首先利用自相关分析消除故障信号中噪声干扰,提取周期成分;然后再用VMD算法将消噪信号分解成若干本征模态分量(intrinsic mode function,IMF),运用能量算子对相关系数及峭度值较大分量进行解调分析;最后通过能量解调谱来判别滚动轴承故障类型。将该方法应用到滚动轴承仿真故障数据和实测数据中,结果表明,该方法可降低了噪声的干扰,有效提取故障特征频率,能够实现滚动轴承故障的精确诊断。
[Abstract]:The fault signals of rolling bearings show non-stationary and multi-component modulation characteristics, and the modulation characteristics of early fault signals are weak, which are easily disturbed by the noise of the surrounding equipment, resulting in the early fault features of bearings being submerged in the noise signals, and the fault features are difficult to extract. In this paper, a method of bearing fault feature extraction based on variable-mode decomposition (VMD) and autocorrelation analysis is proposed. First, the autocorrelation analysis is used to eliminate the noise interference in the fault signal, and the periodic component is extracted. Then the de-noising signal is decomposed into several intrinsic mode function IMFGs by using VMD algorithm, and the correlation coefficient and large kurtosis component are demodulated by energy operator. Finally, the fault types of rolling bearing are identified by energy demodulation spectrum. The method is applied to the rolling bearing simulation fault data and the measured data. The results show that the method can reduce the noise interference, extract the fault characteristic frequency effectively, and realize the accurate diagnosis of rolling bearing fault.
【作者单位】：石家庄铁道大学电气与电子工程学院;
【基金】：国家自然科学基金(U1534204,11372199,11572206) 河北省然科学基金(A2014210142)资助项目
【分类号】：TH133.33

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