Abstract:

Based on the research of  LCD,this paper borrowed ideas from the sifting based signal decomposition methods such as EMD   and LCD,and then presented  a new mono-component signal with physically meaningful instantaneous frequencies, i.e.,ICC,for  restraining the mode mixing problem of LCD and improving the deficiency in terms of the mean curve definition. Finally, a new signal decomposition method,ACD was proposed.Meanwhile, the evaluation criterion of  ICC was also given.In the sifting procedure for separating certain order components,a  set of ICCs was obtained by using different mean curves and compact coefficients. The optimal ICC for this order sifting would  be selected from the candidate ICCs using the evaluation criterion of ICC, which guaranteed  ACD outperforms LCD.The simulation results indicate that the decomposition effect of ACD is better than  that  of  EMD,LCD,ensemble empirical mode  decomposition(EEMD) and autonomous compact local characteristic-scale decomposition(ACLCD),  experimental  results  of  the experimental signals show its validity.Thus a new way for fault diagnosis of rotating machinery is provided.

Key words: adaptive characteristic-scale decomposition(ACD);local characteristic-scale , decomposition(LCD);empirical mode decomposition(EMD);intrinsic compact-scale component(ICC);fault , diagnosis

摘要：

针对局部特征尺度分解（LCD）存在的模态混叠问题和其在均值曲线定义方面存在的不足，在对LCD方法研究的基础上,充分借鉴经验模态分解（EMD）和LCD等此类基于筛分的信号分解方法的思路，定义了一种新的瞬时频率具有物理意义的单分量信号——内禀致密尺度分量（ICC），并提出了一种新的自适应信号分解方法——自适应特征尺度分解（ACD）方法。同时，给出了ICC分量评价准则，通过对ACD每阶筛分中由不同均值曲线和致密系数取值得到的一组不同的分解分量进行对比，选取最优分量作为该阶筛分的ICC分量，从而保障最终分解效果优于LCD方法分解效果。对仿真信号的分析结果证实了ACD方法的分解效果优于EMD、LCD、总体平均经验模态分解（EEMD）和自主致密局部特征尺度分解（ACLCD）方法的分解效果；对实验数据的分析结果验证了ACD的有效性，从而为旋转机械故障诊断提供了一种新的方法。

关键词: 自适应特征尺度分解, 局部特征尺度分解, 经验模态分解, 内禀致密尺度分量, 故障诊断