故障轴承振动信号具有分形特征,可以利用分形维数有效识别变速器轴承的故障模式.噪声的存在对分形维数的计算结果影响较大,为此采用经验模态分解(EMD)方法,对变速器轴承振动信号进行EMD分解,计算分解后的IMF分量的分形维数,提取出变速器轴承不同技术状态下的故障特征.对实测变速器轴承振动信号分析,结果表明:EMD能对不同频带信号进行有效分离；特定IMF分量的分形维数能敏感反应变速器轴承技术状态,可以作为变速器轴承故障诊断的特征参数；EMD与分形维数相结合是提取变速器轴承故障特征的一种有效方法.%Since the vibration signal of faulty bearing has fractal characteristics, the fault modes of a transmission bearing can be effectively identified by using fractal dimensions. However, the noise in the signal has a serious influence on the fractal dimension calculation, so empirical mode decomposition (EMD) method is necessary for solving the problem. In this article, the vibration signal of the transmission bearing was decomposed into intrinsic mode function (IMF) components by EMD, and then the fractal dimensions of these components were calculated to extract the fault characters of the transmission bearing in different conditions. The results show that EMD method is able to separate signals in different frequency bands effectively, and the fractal dimensions of specific IMF components are able to reflect the working state of the transmission bearing sensitively, which can be selected as the characteristic parameters to diagnose the transmission bearing' s fault. The combination of EMD and fractal dimension is an effective method of transmission bearing' s fault character extraction.
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