利用奇异值分解和变分模态分解对滚动轴承非平稳信号进行降噪。

Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings.

机构信息

School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China.

Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK.

出版信息

Sensors (Basel). 2021 Dec 28;22(1):195. doi: 10.3390/s22010195.

In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds.

鉴于滚动轴承的振动信号在早期失效阶段会受到大量噪声的污染，本文提出了一种新的去噪 SVD-VMD 方法，该方法结合了奇异值分解（SVD）和变分模态分解（VMD）。SVD 用于确定潜在模型的结构，即信号子空间和噪声子空间，而 VMD 用于将原始信号分解为几个带限模态。然后，根据奇异值和峰度值的差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，根据差分谱（DS），从这些模态中选择有效分量，重建去噪信号。使用该方法对滚动轴承故障的模拟信号和实验信号进行了分析，并与 SVD-DS 进行了比较。结果表明，该方法能够有效地保留有用信号，并在极其嘈杂的背景下对轴承信号进行去噪。