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...
Main Authors: | , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
John Wiley and Sons Inc
2023
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Subjects: | |
Online Access: | View Fulltext in Publisher |
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filtering used in wavelet transform (WT)-based and empirical mode decomposition (EMD)-based de-noising methods is no longer applicable.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. |
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ISBN: | 25778196 (ISSN) |
ISSN: | 25778196 (ISSN) |
DOI: | 10.1002/eng2.12677 |