摘要
提出基于自相关的振动信号经验模态分解(empirical mode decomposition,EMD)方法,该方法的步骤为,首先对振动信号进行自相关处理,然后再用EMD方法进行分解。该方法与直接用EMD分解的方法进行相比,具有如下优点,能把受到严重干扰的信号的主要振动模态更清晰地分解出来;不用信号延拓就可以获得较好的分解效果,避免了延拓不好对EMD分解效果的影响。研究结果表明,该方法相对直接EMD分解的方法能更好地把主要的振动模态从振动信号中分解出来。该方法可广泛用于旋转机械振动信号时频分析领域。
The autocorrelation-based empirical mode decomposition（EMD） method was proposed. Steps of the method is that, first, applied autocorrelation process to the vibration signal, then the processed signal was analyzed using EMD. A simulate and a real signal were researched using the method and normal EMD＇ s. It is pointed that the method has some merits compared to the normal EMD＇ s, it can extract main vibration components from seriously noised signal more clearly, the method can attain good decomposition effect without data extension, so, bad data extension affected EMD decomposition effect was avoided. The results indicate that compared to the normal EMD the proposed method can extract main vibration components from vibration signal more clearly. The suggested method can be applied widely to vibration signal time-frequency analysis field in rotating machinery.
出处
《机械强度》
EI
CAS
CSCD
北大核心
2007年第3期376-379,共4页
Journal of Mechanical Strength
基金
国家自然科学基金资助项目(50675194)~~
关键词
旋转机械
振动信号
自相关
经验模态分解
Rotating machinery
Vibration signal
Autocorrelation
Empirical mode decomposition（EMD）
作者简介
胡劲松，男，1971年2月生，浙江奉化人，汉族。宁波工程学院电信学院副教授，工学博士，出站博士后，主要从事智能检测、信号处理与故障诊断等技术研究。
