To mitigate the pervasive noise interference present in the measured vibration signals of radial steel gates and to address the limitations of conventional wavelet-threshold denoising, this study proposes a coupled “decomposition–denoising” theoretical framework for vibration-signal purification. The key novelty lies in a smooth and tunable thresholding strategy that enables controlled filtering while preserving key structural characteristics within an integrated denoising workflow. In the proposed approach, the measured signal is decomposed into intrinsic mode components using a data-driven decomposition method, noise-dominated components are identified using multiscale permutation entropy, and only these components are selectively denoised before signal reconstruction. Both qualitative and quantitative analyses conducted on synthetic signals demonstrate the effectiveness of the proposed framework and confirm the enhanced smoothness and robustness of the improved thresholding scheme. Performance is evaluated using objective measures such as signal-to-noise ratio and root-mean-square error, together with spectral-consistency checks for field measurements. Furthermore, two field-measured engineering cases involving radial steel gates substantiate the engineering applicability and generalization capability of the proposed method, showing clearer signals and more stable diagnostic-relevant indicators. Finally, the study integrates the decomposition, denoising, and parameter-selection modules into a user-oriented vibration-signal denoising system, establishing an efficient workflow for engineering signal processing and subsequent structural-health monitoring applications.
Wang et al. (Fri,) studied this question.