齿轮点蚀的多通道数据融合识别方法

Multi-channel Data Fusion for the Identification of Gear Pitting

针对齿轮箱振动信号中混杂其他零部件振动频率的问题,提出一种基于小波包分解独立分量分析(wavelet package independent component analysis,简称WPICA)和多维经验模式分解(multivariate empirical mode decomposition,简称MEMD)的齿轮箱齿面点蚀故障信号的多通道数据融合识别方法。首先,利用一种窄带独立分量分析(sub-band decomposition independent component analysis,简称SDICA)方法—WPICA,从水泵机组多通道信号中提取齿轮箱振源,确定齿轮箱振动包含的特征频率成分;其次,借助MEMD分解多通道机组振动信号,将所获得的多维固有模式函数(intrinsic mode function,简称IMF)进行矩阵互信息运算,完成多通道数据的融合;最后,通过定义IMF故障敏感因子,确定故障敏感IMF的阶数并获得了齿轮点蚀故障的特征频率。数据分析结果证明了本研究方法的有效性。

Aiming to separate the vibration signals of the gearbox from the other disturbing components,a new multi-channel data fusion procedure,combining the wavelet package independent component analysis(WPICA)and multivariate empirical mode decomposition(MEMD),is proposed for the identification of gear pitting.First,the gearbox vibration source is extracted by applying the WPICA,which is a kind of sub-band decomposition independent component analysis(SDICA)to the multi-channel pump-set signals.Second,multi-dimensional IMFs are obtained through the decomposition of MEMD.The mutual information between different IMF matrices in order to implement the formulation of fault sensitive intrinsic mode function(IMF)is calculated at the last step.In case the fault sensitive IMF is found out,the frequency dominated in this IMF is determined as the characteristic frequency of gear pitting.Data analysis shows the efficiency of the proposed procedure.