Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD,...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2021-07, Vol.16 (7), p.e0254747-e0254747
Main Authors: Gao, Kangping, Xu, Xinxin, Li, Jiabo, Jiao, Shengjie, Shi, Ning
Format: Article
Language:English
Subjects:
Analysis
Background noise
Bearing strength
Decomposition
Decomposition method
Diamond tools
Empirical analysis
Engineering
Engineering and Technology
Evaluation
Fault diagnosis
Feature extraction
Identification and classification
Information processing
Laboratories
Machinery
Magneto-electric machines
Methods
Multilayers
Noise
Noise control
Noise reduction
Physical Sciences
Polycrystalline diamond
Research and Analysis Methods
Rotating machinery
Signal processing
Spectral analysis
Spectrum analysis
Standard deviation
Vibration
Vibration analysis
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0254747