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

• 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
• Subjects: Anomaly detection; dual-pulse characteristic; Dual-pulse characteristic; Dual-pulses; empirical mode decomposition; Empirical mode decomposition; Empirical Mode Decomposition; Local mean decompositions; multi-level local mean decomposition; Multi-level local mean decomposition; Multilevels; Outlier Detection; outlier detection and waveform smoothing; Outlier detection and waveform smoothing; Pulse characteristics; Roller bearings; signal de-noising; Signal denoising; Signal de-noising; Statistics; the superposition and recombination; The superposition and recombination; Waveform smoothing; Wavelet decomposition
• Online Access: View Fulltext in Publisher

 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filtering used in wavelet transform (WT)-based and empirical mode decomposition (EMD)-based de-noising methods is no longer applicable.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.