针对实验中采集到的滚动轴承的振动信号具有成分复杂和较强的非平稳性等特点，本文提出采用基于遗传算法优化的匹配追踪算法(GAMP)和总体平均经验模式分解(EEMD)相结合的方法，实现对滚动轴承振动信号的处理与分析。首先，利用GAMP算法将滚动轴承振动信号线性展开成能够较好的匹配该信号特征结构的一系列高斯函数，达到消除干扰噪声锁定信号的局部特征的目的；然后，针对GAMP消噪后的振动信号中可能存在的虚假频率成分或不连续的分量，利用EEMD方法来予以剔除，通过傅里叶变换将处理后的振动信号从时域转化到频域，提取出故障振动信号的故障频率；最后，采用支持向量机(SVM)对滚动轴承的正常和故障振动信号进行分类，实现了对滚动轴承的故障诊断。
Abstract
Based on genetic algorithm to optimize the matching pursuit algorithm (GAMP) and ensemble empirical mode decomposition (EEMD),a rolling bearing fault diagnosis method is proposed. It can achieve the purpose of processing and analyzing rolling bearing vibration signals which have the characteristics of complex components and strong non-stationary in the experiment. First, the rolling bearing vibration signals can be linearly expanded into a series of Gaussian functions which can better match characteristic structure of signals by GAMP algorithm. The purpose of eliminating interference noise and locking the local characteristics of the signals is achieved. Second, EEMD method is used to eliminate the false frequency components and discontinuous components that may exist in the vibration signals of GAMP. The processed vibration signals are transformed from the time domain to frequency domain by FFT, and the fault frequency of the fault vibration signals is extracted. At last, Support Vector Machine(SVM) is used to classify the normal and fault vibration signals of the rolling bearing, and the rolling bearings fault is diagnosed.
关键词
GAMP /
EEMD /
SVM /
滚动轴承 /
故障诊断
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Key words
GAMP /
EEMD /
Rolling bearings /
Fault diagnosis
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参考文献
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