This paper presents a novel vibration signal fusion algorithm using improved empirical wavelet transform and variance contribution rate to fuse three-channel vibration signals for weak fault detection of hydraulic pumps. Firstly, empirical wavelet transform (EWT) is utilized to decompose the three-channel signals into several AM–FM components. Then in accordance with the statistical characteristics of these component data, variance contribution rate is defined to measure the weight of component data points. A series of fusion coefficients are computed and assigned to every component point. Finally, these component points are fused into one single signal and Hilbert transform is conducted to demodulate the fault characteristic frequency for weak fault detection. Moreover, to address the issue of improper EWT spectrum segmentation, we introduce Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to improve EWT in the full space and the frequencies corresponding to outlier points are taken as the boundaries of spectrum segmentation. Therefore, the number of boundaries is more reasonable and the AM–FM components are more consistent with inherent components existing in the vibration signals of pumps. Results of simulation and experiment analysis demonstrate the good performance of the exhibited fusion algorithm in weak fault detection of hydraulic pumps.

本文提出了一种新的振动信号融合算法，该算法利用改进的经验小波变换和方差贡献率融合三通道振动信号，用于液压泵的弱故障检测。首先，经验小波变换（EWT）用于将三通道信号分解为几个AM-FM分量。然后根据这些组成数据的统计特征，定义方差贡献率以测量组成数据点的权重。计算一系列融合系数并将其分配给每个组成点。最后，将这些组成点融合为一个信号，并进行希尔伯特变换以解调故障特征频率，以进行弱故障检测。此外，为解决EWT频谱分割不当的问题，我们引入了基于密度的噪声应用空间聚类（DBSCAN），以改善整个空间中的EWT，并且将与离群点相对应的频率视为频谱分割的边界。因此，边界数量更加合理，AM-FM分量与泵振动信号中存在的固有分量更加一致。仿真和实验分析结果表明，所展示的融合算法在液压泵弱故障检测中具有良好的性能。边界数量更合理，并且AM-FM分量与泵振动信号中存在的固有分量更加一致。仿真和实验分析结果表明，所展示的融合算法在液压泵弱故障检测中具有良好的性能。边界数量更合理，并且AM-FM分量与泵振动信号中存在的固有分量更加一致。仿真和实验分析结果表明，所展示的融合算法在液压泵弱故障检测中具有良好的性能。