基于多尺度熵的滚动轴承故障程度评估  

Assessment of rolling element bearing fault severity using multi-scale entropy

作　　者：张龙[1] 黄文艺[1] 熊国良[1] 

机构地区：[1]华东交通大学机电工程学院,南昌330013

出　　处：《振动与冲击》2014年第9期185-189,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(51205130;51265010);江西省教育厅科技项目(GJJ12318);江西省自然科学基金项目(20132BAB216029)

摘　　要：轴承故障严重程度评估是实现视情维修的基础。滚动轴承出现故障时,其振动信号将表现出非平稳性和非线性。基于多尺度熵方法,构建了一个同时考虑多个尺度上熵值均值大小和熵值变化趋势的轴承故障程度描述新指标——多尺度熵均偏值(PMME)。仿真实验和轴承疲劳实验数据分析表明该指标能够较早地发现轴承早期故障,并且能很好地跟踪故障发展趋势。Quantitative identification of bearing fault severity is the basis of condition-based maintenance.The vibration signals exhibits nonstationarity and nonlinearity in the presence of bearing faults.On the basis of the multi-scale entropy,a new index quantitatively describing bearing fault severity was constructed taking the mean value and the variations of the entropies over multiple scales into account.The new index was called partial mean of multi-scale entropy （PMME）.Simulation and experimental results showed that the new index is able to detect incipient bearing fault and trace the fault development well.

关 键 词：故障程度评估 故障诊断 多尺度熵 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911[电子电信—通信与信息系统]