摘要
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 rolling bearing vibration signal can be decomposed into a number of intrinsic mode functions （IMF） adaptively according to its own scale with the empirical mode decomposition （EMD）method.A rolling bearing failure will change distributions of IMF energy,and a bearing fault diagnosis can be realized by establishing the relationship between IMF energy distributions and bearing conditions based on the gray relational grade theory.Here,in order to improve the defects of the traditional gray analysis in pattern recognition,a gray similar relational grade model reflecting a curve＇s shape features was proposed based on the similarity of slope.Then,combined with the traditional approaching relativity model,a gray comprehensive relativity diagnosis model reflecting both a curve＇s position and its shape features was constructed.The simulation results showed that the new model can be used to recognize rolling bearing faults more effectively and accurately.
出处
《振动与冲击》
EI
CSCD
北大核心
2014年第3期197-202,共6页
Journal of Vibration and Shock
基金
2011年度国家自然科学基金项目(51175511)
2012年度江苏省(自然科学基金)青年基金项目(BK2012061)
关键词
经验模态分解
内禀模态能量
灰色综合关联度
滚动轴承
故障诊断
empirical mode decomposition （EMD）
intrinsic mode function （IMF）energy
gray synthetically relational grade
rolling bearing
fault diagnosis
