基于EMD的滚动轴承故障灰色诊断方法

经验模态分解（EMD）方法可使滚动轴承振动信号根据自身尺度自适应地分解为若干个内禀模态分量（IMF），滚动轴承发生故障会导致振动能量在各IMF分量上的分布发生变化，结合灰色关联模型建立IMF能量分布与轴承状态之间的对应关系，可实现轴承的状态识别。为改善传统灰关联分析在模式识别方面的缺陷，基于斜率相似的原理构造了能反映曲线形状信息的相似关联度模型，结合传统的接近关联度模型建立了能同时反映曲线位置与形状特性的灰色综合关联度诊断模型。算例结果表明，该方法能准确有效地实现滚动轴承的故障诊断。

A grey fault diagnosis method for rolling bearing based on EMD

The rolling bearing vibration signal can be decomposed into a number of intrinsic mode functions (IMF) adaptively according to its own scale by the empirical mode decomposition (EMD) method. The rolling bearing failure will vary the distribution of IMF energy, and the bearing fault diagnosis can be realized by establishing the relationship between the IMF energy distributions and bearing conditions based on the gray relational grade theory. In order to make up for the defects of the traditional gray analysis in pattern recognition, a gray similar relational grade model which can reflect the curve’s shape feature was proposed based on the similarity of the slope. Then combined with the traditional approaching relativity model, the gray comprehensive relativity diagnosis model which can reflect both the curve’s position and shape features was constructed. The simulation results show that the new model can recognize rolling bearing faults more effectively and accurately．