基于流形学习的滚动轴承故障盲源分离方法

滚动轴承的故障信号是一种典型的非线性非平稳信号,其信号中常常混有噪声信号及其他干扰成分。提出了一种基于流形学习的滚动轴承故障盲源分离方法,首先,利用经验模态分解(empirical mode decomposition,简称EMD)对单通道模拟信号进行分解,对得到的多通道信号构造其协方差矩阵,计算矩阵的奇异值下降速比得到原始信号数目;其次,利用峭度等指标选择最优观测信号,利用核主成分分析(kernel principal components analysis,简称KPCA)提取信号的流形成分;最后,利用快速独立成分分析(fast independent component analysis,简称Fast ICA)还原得到源信号。该方法不但解决了故障信号的欠定盲源分离问题,还提出了最优观测信号的确定准则,并通过实例验证了方法的有效性。

Blind Source Separation Method for Rolling Bearing Faults Based on Manifold Learning

The fault signal of rolling bearing is a kind of non-linear and non-stationary signal, and it is often mixed with noise signals and interference components. Therefore, it is important to effectively separate the bearing fault source signal and diagnose it. A blind source separation method for rolling bearing faults based on manifold learning is proposed. Firstly, the measured single channel signal is subjected to empirical mode decomposition (EMD) to construct a multi-channel test signal, and then the source number is determined by the deceleration ratio of the singular value of the multi-channel test signal covariance matrix. Then the kurtosis and other indicators are used to select the optimal observation signal. After that, Kernel principal components analysis (KPCA) is used to extract the stream formation of the signal, and finally fast independent component analysis (FastICA) is used to restore the source signal. This method not only solves the problem of underdetermined blind source separation of fault signals, but also proposes the criteria for determining the optimal observed signals. An example is executed to verify the effectiveness of the method.