基于EMD的滚动轴承故障诊断方法研究
|
摘要
滚动轴承安装在机体内部,运行中故障不易被及时发现,致使故障扩大甚至造成严重经济损失。单一运用滚动轴承故障诊断方法,无法精准反映其故障全部特征。以经验模态分解(EMD)方法为基础,提取含有故障特征信息的不同阶本征模函数(IMF);选择较大的峭度值,用于筛选各阶IMF分量;运用Hilbert变换进行包络解调,实现对重构信号的包络谱分析;进行频域变换得到精确的轴承故障特征信息,并将其与它的故障特征频率比对,实现故障判断。试验结果表明,该方法能够对滚动轴承进行准确故障诊断,具备良好的工程应用价值。
The rolling bearing is installed in the body,and the fault is not easy to be found in time,which leads to the expansion of the fault and even serious economic losses. The single fault diagnosis method of rolling bearing can not accurately reflect all the characteristics of its fault. Empirical mode decomposition(EMD) is used to obtain the intrinsic mode function(IMF) of each order containing fault feature information. Select a large kurtosis value to filter each order IMF component. The accurate bearing fault feature information is obtained by frequency domain transformation,and compared with its fault feature frequency to realize fault diagnosis. The Experiment results show that the method can diagnose rolling bearing accurately and has good engineering application value.
The rolling bearing is installed in the body,and the fault is not easy to be found in time,which leads to the expansion of the fault and even serious economic losses. The single fault diagnosis method of rolling bearing can not accurately reflect all the characteristics of its fault. Empirical mode decomposition(EMD) is used to obtain the intrinsic mode function(IMF) of each order containing fault feature information. Select a large kurtosis value to filter each order IMF component. The accurate bearing fault feature information is obtained by frequency domain transformation,and compared with its fault feature frequency to realize fault diagnosis. The Experiment results show that the method can diagnose rolling bearing accurately and has good engineering application value.
引文
[1]杨晨.基于振动信号分析法的滚动轴故障诊断研究[D].兰州:兰州理工大学,2014.
[2]吕志民.分形维数及其在滚动轴承故障诊断中的应用[J].机械工程学报,1999,6(1):453-458.
[3]佘博,田福庆,梁伟阁.基于MED和EEMD的滚动轴承故障诊断方法[J].海军工程大学学报,2017,29(1):107-112.
[4]周浩,贾民平.基于EMD和峭度的Hilbert包络解调在滚动轴承故障诊断中的应用分析[J].机电工程,2014,31(9):1136-1167.
[5]牛家骅.基于EEMD和SVM联合诊断的发动机故障分析[D.呼和浩特:内蒙古工业大学,2015.
[6]任远杰.基于改进谱峭度的滚动轴承早期故障诊断研究[D].大连:大连理工大学,2016.
[7]武坚,安宏勇,冯思桐.故障诊断中的数据建模与特征选择[J].电脑知识与技术,2016,12(25):207-209.
[8]赵志强.基于集合经验模式分解与径向基函数神经网络的齿轮故障诊断[D].邯郸:河北工程大学,2015.
[9]杨建鸣,冯利平,高立新,等.高速线材轧机齿轮箱在线检测数据分析[J].机械设计与制造,2015,36(3):145-147.
[10]张良,杨涛.基于多重自相关与包络谱分析的滚动轴承故障诊断方法研究[J].组合机床与自动化加工技术,2017,54(8):93-96.
[2]吕志民.分形维数及其在滚动轴承故障诊断中的应用[J].机械工程学报,1999,6(1):453-458.
[3]佘博,田福庆,梁伟阁.基于MED和EEMD的滚动轴承故障诊断方法[J].海军工程大学学报,2017,29(1):107-112.
[4]周浩,贾民平.基于EMD和峭度的Hilbert包络解调在滚动轴承故障诊断中的应用分析[J].机电工程,2014,31(9):1136-1167.
[5]牛家骅.基于EEMD和SVM联合诊断的发动机故障分析[D.呼和浩特:内蒙古工业大学,2015.
[6]任远杰.基于改进谱峭度的滚动轴承早期故障诊断研究[D].大连:大连理工大学,2016.
[7]武坚,安宏勇,冯思桐.故障诊断中的数据建模与特征选择[J].电脑知识与技术,2016,12(25):207-209.
[8]赵志强.基于集合经验模式分解与径向基函数神经网络的齿轮故障诊断[D].邯郸:河北工程大学,2015.
[9]杨建鸣,冯利平,高立新,等.高速线材轧机齿轮箱在线检测数据分析[J].机械设计与制造,2015,36(3):145-147.
[10]张良,杨涛.基于多重自相关与包络谱分析的滚动轴承故障诊断方法研究[J].组合机床与自动化加工技术,2017,54(8):93-96.