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The viscoelastic sandwich cylindrical structure (VSCS) is widely applied in aerospace, transportation, etc. Its health is closely related to the security of its own service and the entire set of equipment. Therefore, it is very important to detect its operating state. With a focus on the difficulty of weak feature extraction and the lack of efficient interlayer slipping detection indexes for the VSCS, this paper proposes a generalized variational mode decomposition (GVMD) method to extract the weak feature of the VSCS, and constructs a detection index to identify interlayer slipping fault. To this end, first, a GVMD method is proposed to decompose the original signal into a set of band-limited components (called variational mode functions, VMFs) of interest by taking full advantage of prior information such as spectral locations and bandwidths. Then, the vibration signals from the symmetric positions of the VSCS are decomposed by GVMD to extract the VMFs as expected. Finally, the interlayer slipping detection index, namely the normalized energy of the VMFs, is constructed to identify the interlayer slipping fault. And the interlayer slipping symptoms are explored. The effectiveness of GVMD and the proposed detection index is verified by the simulation and the experiment. The results show that: (1) compared with VMD and complementary ensemble empirical mode decomposition, GVMD can expectedly extract feature components (including weak feature components) owing to its excellent performance. (2) The proposed detection index can effectively identify the interlayer slipping fault of the VSCS, and the frequency components near 2×  are sensitive to interlayer slipping fault. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]