摘要
提出了一种基于经验模态分解EMD(Emp iricalMode Decomposition)的齿轮裂纹故障诊断的新方法。EMD方法具有自适应的信号分解和降噪能力,EMD是先把时间序列信号,分解成不同特征时间尺度的固有模态函数(In-trinsic Mode Function,简称IMF),然后通过选取表征齿轮裂纹故障的IMF分量进行功率谱分析,就可提取齿轮故障振动信号的特征。齿轮故障实验信号的研究结果表明:该方法能有效地识别齿轮的齿根裂纹故障。
A novel method for fault diagnosis of gear crack based on EMD(Empirical Mode Decomposition) is(presented).EMD method is a self-adaptive method in regard to non-stationary and non-linear signal.The methodology(developed) in the paper decomposes the original time series data into intrinsic oscillation modes,using the empirical mode decomposition.Then the power spectrum analysis is applied to the selected intrinsic mode function which stands for the gear crack fault.The basic principle is introduced in detail.The experimental results show that this method based on EMD can(effectively) recognize the faults of gear crack.
出处
《振动与冲击》
EI
CSCD
北大核心
2006年第1期133-135,145,共4页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(50375157)
关键词
故障诊断
齿轮
经验模态分解
信号处理
fault diagnosis,gear,empirical mode decomposition,signal processing
