基于EMD与ICA的滚动轴承复合故障诊断

针对单通道情况下滚动轴承复合故障难以分离问题, 提出基于经验模式分解 (empirical mode decomposition, EMD) 的独立分量分析 (independent component analysis, ICA) 算法.该方法首先对单通道采集的轴承复合故障信号进行EMD分解, 得到多个基本模式分量函数 (intrinsic mode function, IMF) , 然后依据帩度指标及相关系数值, 选取有效的IMF分量与原观测信号组成新的观测信号, 对其进行ICA处理, 进而实现轴承复合故障的分离.实验结果表明, 该方法可有效地分离轴承早期的复合故障.

Composite Fault Diagnosis of Rolling Bearings Based on EMD and ICA Algorithm

Aiming at the difficulty of separating rolling bearing composite faults in the case of single channel signal,a new algorithm based on empirical mode decomposition( EMD) and independent component analysis( ICA) was proposed in this paper. First,the composite bearing fault signals collected for a single channel were decomposed by EMD to obtain some intrinsic mode function( IMF).Then,main components were confirmed by calculating the correlation coefficient of every IMF and original composite signal and kurtosis value of every IMF. At the same time,main components with original signal were processed by ICA to realize the separation of composite fault of rolling bearings.Experimental results show that this method can effectively separate early rolling bearings composite fault.