1.School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China; 2.Huaneng Power International Co., Ltd., Hunan Clean Energy Branch, Changsha 410015, China
Abstract:The components of gearbox vibration signals under variable speed conditions usually have time-frequency overlap and cross-band features, which makes it very difficult to directly separate the components. To this end, this paper introduces a new multichannel multicomponent decomposition (MMD) method, and utilizes a new group intelligent optimization algorithm - Marine Predators Algorithm (MPA) to solve the key optimization problem in the MMD method, and then proposes the MPA-MMD (MPA-MMD) method based on MPA optimization. The MPA-MMD method represents each component as a linear combination of a set of weighted feature vectors, and is particularly suitable for decomposing complex signals with time-frequency overlap or cross-band features because it does not rely on time-scale features. By setting up noisy simulation signals with component overlap, cross-band and fluctuation features, MPA-MMD is compared with MMD based on other optimization algorithms and multi-channel variational mode decomposition (MVMD). The results show the advantages of MPA-MMD in terms of decomposition effect, convergence and noise suppression. On this basis, MPA-MMD is applied to the feature extraction of gearbox vibration signals under variable speed conditions, which have complex features of component overlap and cross-band. The targeted experimental signal analysis results show that MPA-MMD can directly and accurately obtain the fault components affected by speed.
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