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中文摘要：
针对滚动轴承故障信号具有非平稳性的问题,对滚动轴承非平稳信号特征提取问题进行了研究,提出了基于经验模态分解（EMD）和主成分分析（PCA）的滚动轴承故障信号特征提取方法。运用经EMD对滚动轴承故障信号进行分解,得到了多个本征模态分量（IMF）,计算了每个IMF的总能量值,取能量集中的前6层IMF,将每层IMF频率集中的部分等分成多段,计算每段能量值,作为该故障的特征值。运用PCA对特征值进行了维度缩减,将高维度的特征值降低为低维度的特征值,计算了特征值的累计贡献率,取累计贡献率达到80%的前多个特征值作为每组故障的特征值。研究结果表明,该方法可有效提取滚动轴承故障信号特征,实现高维特征维度缩减。
英文摘要：
Aiming at the problem that fault signal of rolling bearing was non-stationary,aiming at which,how to extract feature of non-stationary signal was studied and the methods based on empirical mode decomposition（ EMD） and principal component analysis（ PCA） was put forward. Signal was decomposed into several IMFs by means of EMD. The energy of every intrinsic mode function（ IMF） was calculated,and some IMFs with greater energy was chosen. Every IMF was divided into several segments according to the range the frequency,and energy of every segment was calculated as the feature value. PCA was used to reduce the dimensions of feature value. Accumulative contribution of feature value was calculated and the first few feature values whose accumulative contribution rate reached 80% was chosen as the final fature value. The results indicate that the method is valid for the feature extraction of rolling bearing,and dimension reduction of high dimensional feture.
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