Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps

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. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.