基于局部特征尺度分解和奇异值差分谱的滚动轴承故障诊断方法

发布时间：2018-01-09 22:27

本文关键词：基于局部特征尺度分解和奇异值差分谱的滚动轴承故障诊断方法　出处：《机械设计与研究》2017年04期 　论文类型：期刊论文

【摘要】：为有效消除滚动轴承故障振动信号中噪声,更好提取故障特征频率,提出了基于局部特征尺度分解和奇异值差分谱的故障诊断方法。该方法首先利用局部特征尺度分解将非平稳信号分解成若干个不同频带的内禀尺度分量;然后对包含故障特征的分量构造Hankel矩阵并进行奇异值分解并求得奇异值差分谱曲线,利用差分谱最大突变点确定重构信号的阶数并重构信号;最后再求重构信号的包络谱,便可准确获得相应故障特征频率。滚动轴承故障诊断的试验结果证明,该方法能快速准确地提取出故障信息,具有一定的优势。
[Abstract]:In order to effectively eliminate the noise in the rolling bearing fault vibration signal, the fault feature frequency can be extracted better. A fault diagnosis method based on local eigenscale decomposition and singular value differential spectrum is proposed. Firstly, the non-stationary signal is decomposed into intrinsic scale components of different frequency bands by using local eigenscale decomposition. Then the Hankel matrix is constructed and singular value decomposition is carried out to obtain the singular value differential spectral curve. The order of the reconstructed signal is determined by the maximum mutation point of the differential spectrum and the reconstructed signal is reconstructed. Finally, the envelope spectrum of the reconstructed signal can be obtained accurately. The test results of rolling bearing fault diagnosis show that the method can extract fault information quickly and accurately, and has some advantages.
【作者单位】：重庆电力高等专科学校信息工程学院;重庆工业职业技术学院自动化学院;
【基金】：重庆市教委教育教学研究资助项目(C-JY201404)
【分类号】：TH133.33
【正文快照】： 滚动轴承在机械设备中的应用十分广泛,对设备运行状态起着至关的作用,对其故障诊断进行研究十分必要。在滚动轴承的故障诊断中,根据振动信号对其进行状态监测和故障诊断是比较常用的方法。但其振动信号往往具有非线性、非平稳性等特点且包含有强烈的噪声,如何从这些信号中提取

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