Jixie chuandong (Apr 2019)
Fault Feature Extraction of Gearbox based on Adaptive VMD
Abstract
In the noisy environment， the composite fault feature extraction is more difficult. The VMD is widely used in gearbox fault diagnosis， but it is a parametric decomposition method. If K is too large or too small， it will lead to over-decomposition or under-decomposition. The number of layers needs to be determined adaptively， a multi-point kurtosis -VMD （Variational Mode Decomposition） composite fault feature extraction method is proposed. Considering the multi-point kurtosis， the number of impact cycles of multiple faults can be extracted， the number of periodic impacts determines the number K of decomposition layers of the VMD， and after VMD processing， the fault features are further determined by FFT. The proposed adaptive composite fault feature extraction method and Ensemble Empirical Mode Decomposition （EEMD）comparison analysis verify that it can overcome the characteristics of modal aliasing. The effectiveness of this method is further determined by the measured signal processing. The composite fault characteristics such as gear spalling and bearing balls are finally determined.
