An optimized variational mode decomposition for extracting weak feature of viscoelastic sandwich cylindrical structures. (16th February 2018)

• Record Type: Journal Article
• Authors: Guo, Yanfei; Zhang, Zhousuo; Cao, Jianbin; Gong, Teng; Yang, Wenzhan
• Is Part Of: Measurement science & technology. Volume 29:Number 3(2018:Mar.)
• Publication Date: 2018-02-16
• Subjects: variational mode decomposition -- parameter optimization -- weak feature extraction -- bandwidth -- viscoelastic sandwich cylindrical structure; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aa9ef0
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Variational mode decomposition (VMD), which is an alternative to empirical mode decomposition (EMD), has been widely used to extract the feature components of nonstationary signals. However, as a parameterized method, the performance of VMD is heavily influenced by its parameters. Meanwhile, it cannot efficiently extract weak feature components submerged in powerful ones. To address these problems, a novel method based on an optimized VMD is developed for precisely extracting the weak feature of viscoelastic sandwich cylindrical structures (VSCSs). In this method, a parameter optimization algorithm first is proposed to simultaneously select the crucial parameters in the VMD, and to reveal the characteristics of the influence of these parameters on the decomposition performance. Then, the weak feature components submerged in low-frequency strong ones are extracted twice by using the VMD with the optimized parameters. The effectiveness of the proposed method is verified by simulation signals and the experiment vibration signal collected from the VSCS. Its robustness to noise is also discussed. The results indicate that the parameter optimization algorithm can adaptively obtain optimal parameters, and compared with the optimized complementary ensemble EMD (CEEMD) and the original VMD, the proposed method can precisely extract the weak feature components submerged in strong ones.