An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise...

Full description

Saved in:
Bibliographic Details
Published in:Symmetry (Basel) 2021-04, Vol.13 (4), p.617
Main Authors: Ma, Jianpeng, Zhuo, Shi, Li, Chengwei, Zhan, Liwei, Zhang, Guangzhu
Format: Article
Language:English
Subjects:
adaptive signal processing
Algorithms
Bearing strength
Bearings
Decomposition
Fault diagnosis
Feature extraction
Mathematical models
Methods
Optimization
optimization algorithm
Optimization algorithms
Parameters
Roller bearings
rolling bearing
Signal processing
Signal to noise ratio
Standard deviation
Vibration measurement
Wavelet transforms
White noise
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:When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13040617