An improved method for signal de-noising based on multi-level local mean decomposition

The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy-concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)-based and empirical mode decomposition (EMD)-based de-noising methods is...

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Bibliographic Details
Main Authors: Chen, H. (Author), Jiang, Y. (Author), Jiao, W. (Author), Sun, J. (Author), Tang, C. (Author), Wang, C. (Author), Xia, H. (Author), Xu, C. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2023
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Online Access:View Fulltext in Publisher
Description
Summary:The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy-concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)-based and empirical mode decomposition (EMD)-based de-noising methods is no longer applicable. To address this issue, an improved signal de-noising method is proposed by using the multi-level local mean decomposition (ML-LMD), the superposition and recombination (SR) of high-order PFs, the outlier detection, and waveform smoothing (OD-WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well-known methods like WT-based and EMD-based de-noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals. © 2023 The Authors. Engineering Reports published by John Wiley & Sons Ltd.
ISBN:25778196 (ISSN)
ISSN:25778196 (ISSN)
DOI:10.1002/eng2.12677