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针对非线性、非稳态、含噪原始信号混合且混合信号数目小于源信号数目的旋转机械调制故障源信号盲分离问题,提出了一种基于经验模态分解(EMD)和主成分分析(PCA)相结合的方法.对混合信号进行经验模态分解提取嵌入在信号中的所有振荡模式,应用主成分分析方法对所提取的模式进行共性分析,得到模式中的主要成分.利用该方法对仿真数据和两通道滚动轴承加速度振动数据进行了分析,结果表明,该方法能够有效突出旋转机械的故障特征频率成分,避免了误诊断,且适用范围优于独立分量分析方法.
In order to solve the problem of blind source separation of rotating machinery modulation source signals with non-linear, unsteady and noisy original signals, the number of mixed signals is smaller than the number of source signals, a new method based on empirical mode decomposition (EMD) and principal components analysis (PCA ) Is proposed in this paper.Empirical mode decomposition of mixed signal is used to extract all oscillation modes embedded in the signal.The principal component analysis method is used to carry out common analysis on the extracted modes to get the main components in the model.Using this method, Data and two-channel rolling bearing acceleration vibration data are analyzed. The results show that this method can effectively highlight the fault characteristics of rotating machinery frequency components, to avoid the misdiagnosis, and the scope of application is better than the independent component analysis.