Research on a Denoising Method of Vibration Signals Based on IMRSVD and Effective Component Selection

Xihui Chen (), Xinhui Shi, Chang Liu and Wei Lou
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Xihui Chen: College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Xinhui Shi: College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Chang Liu: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Wei Lou: College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China

Energies, 2022, vol. 15, issue 23, 1-21

Abstract: This paper proposes a denoising method of vibration signal based on improved multiresolution singular value decomposition (IMRSVD) and effective component selection. A new construction method of trajectory matrix is used, which can enhance the oscillating component of the original signal. Next, based on the improved trajectory matrix, singular value decomposition (SVD), which plays the role of pre-decomposition, is used to obtain multiple one-dimensional components, and the further decomposition of that is achieved by multiresolution singular value decomposition (MRSVD). Finally, the effective components selection of a series of decomposed signal components is achieved based on the proposed feature evaluation index (FEI). The denoising experiments are carried out using the simulation signal and the vibration signal of planetary gear, respectively. The experimental results show that the proposed method performs better than the traditional SVD denoising method, and the weak fault feature in the vibration signal can be extracted successfully. In addition, the comparison between periodic modulation intensity (PMI) and FEI displays that the proposed method has better robustness and accuracy than the interference components with similar frequency. Thus, the proposed method is an effective weak fault feature extraction and denoising tool of vibration signals for fault diagnosis.

Keywords: denoising; vibration signal; feature extraction; IMRSVD; FEI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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