Enhanced successive variational mode decomposition with improved fitness function and optimizer for noise analysis of electric vibrators

ZY Xu and ZW Chen, JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 39, 3031-3037 (2025).

DOI: 10.1007/s12206-025-0505-x

This article conducts an in-depth study and improvement of the optimization problem of the core parameter alpha in the successive variational mode decomposition (SVMD) algorithm. We have creatively proposed using the pearson correlation coefficient (PCC) as the fitness function to optimize it. Additionally, we combined this approach with the grey wolf optimizer (GWO) to enhance it. This optimized algorithm was then applied to noise suppression in electric vibrators. Experimental results demonstrate that the improved SVMD algorithm effectively addresses issues such as modal aliasing and endpoint effects, and can adaptively decompose signals with high convergence and fast optimization speed. Compared to existing algorithms, this algorithm improves accuracy by approximately 37.8 % and efficiency by about 65.7 % when applied to noise signal decomposition. The application of this enhanced algorithm in noise signal processing confirms its effectiveness and reliability.

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