基于IMF和预测滤波的轴承故障诊断方法

发布时间：2018-03-25 03:11

  本文选题：固有模态函数　切入点：预测滤波　出处：《组合机床与自动化加工技术》2016年08期

【摘要】：针对滚动轴承早期故障信息难以提取的问题,提出了基于固有模态函数(IMF)和线性预测滤波的诊断技术。首先,通过经验模态分解(EMD)把振动信号分解成一系列的固有模态函数。根据包络频谱相关信息提出了一种固有模态函数重构方法,将故障信息敏感的固有模态函数重构为一个新的信号。然后通过线性预测滤波加强重构后信号的冲击故障信息,最后利用信号的功率谱有效的展现了轴承的故障频率特性。通过实测滚动轴承信号对该方法进行了验证,结果表明该方法能准确的检测滚动轴承故障。
[Abstract]:In order to solve the problem that it is difficult to extract the early fault information of rolling bearing, a diagnosis technique based on inherent mode function (IMF) and linear predictive filtering is proposed. The vibration signal is decomposed into a series of inherent mode functions by empirical mode decomposition (EMD). According to the information of envelope spectrum, a reconstruction method of inherent mode function is proposed. The inherent mode function which is sensitive to fault information is reconstructed into a new signal, and then the impulse fault information of the reconstructed signal is enhanced by linear predictive filtering. Finally, the power spectrum of the signal is used to show the fault frequency characteristics of the bearing effectively, and the method is verified by the measured rolling bearing signal. The results show that the method can accurately detect the fault of the rolling bearing.
【作者单位】： 山东理工大学电气与电子工程学院;山东理工大学机械工程学院;
【基金】：国家自然科学基金(51305243) 山东省自然科学基金(ZR2012EEL06)
【分类号】：TH133.33
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本文编号：1661275