Recursive Sliding Bandwidth-Aware Variational Mode Decomposition for Signal Denoising

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Abstract
Structural health monitoring (SHM) relies heavily on the real-time extraction of valid information from raw streaming vibration data. However, the widely used traditional variational mode decomposition (VMD) method has two critical technical bottlenecks, which include key decomposition parameters that rely on manual empirical setting with unavoidable subjectivity and batch processing mechanisms that cannot support online real-time denoising of continuous monitoring data. To overcome these challenges, this paper develops a novel Recursive Sliding Bandwidth-Aware Variational Mode Decomposition (RSBAVMD) method and carries out a full set of scientific research. Firstly, we introduce the weighted spectrum trend (WST) method to adaptively divide the signal spectrum into sub-bands and automatically solves the optimal number of decomposition modes and the matched penalty factor for each mode, eliminating the dependence on manual parameter tuning. Then, to enable online processing of streaming SHM data, we introduce an online iterative update method based on recursive sliding Fourier transform, thus breaking the offline application limitation of conventional VMD. We conduct validation experiments using numerical simulated signals and measured structural vibration data. The results indicate that the proposed RSBAVMD method achieves adaptive determination of components and penalty factors. Compared with traditional VMD and mainstream online denoising methods, this method has the best denoising effect. Compared with the VMD method, the proposed method reduces analysis time by about 80% and is less affected by the amount of analyzed data. The proposed RSBAVMD method can satisfy the real-time denoising requirements of streaming vibration data while ensuring accuracy, providing reliable support for online SHM and structural state assessment.
Funding Information
  • National Key R&D Program of China (2019YFB1600702)

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