Strain signal denoising based on adaptive Variation Mode Decomposition (VMD) algorithm
Ning Yu, Xuyuan Yang, Renjian Feng, Yinfeng Wu
Abstract
Addressing the problem of vulnerability of the directly measured signal in the field of strain weighing to the high-energy noise of similar frequency bands, an adaptive VMD algorithm is proposed from the perspective of signal separation for the decomposition and denoising of strain signal in the field of strain weighing. In this paper, the adaptive VMD algorithm is used to determine the optimal values of two key parameters, namely, the number of decomposition layers and the penalty factor, to avoid the blindness of parameter selection. The separation results are tested by parameters such as sample entropy, and then the original measurement signal is adaptively decomposed into multiple optimal intrinsic mode function components, and the effective components after extraction are reconstructed into new observation signals. The analysis results of the strain data collected at the weighing site show that the adaptive VMD algorithm can separate and extract the effective strain signal in line with the actual situation from the original strain signal mixed with noise and achieve the purpose of avoiding the interference of high-energy environmental noise with close frequency bands.