An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional suppo...

Full description

Saved in:
Bibliographic Details
Published in:Shock and vibration 2021, Vol.2021 (1)
Main Authors: Bie, Fengfeng, Miao, Yi, Lyu, Fengxia, Peng, Jian, Guo, Yue
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bearings
Classification
Correlation coefficients
Decomposition
Efficiency
Energy
Entropy (Information theory)
Failure modes
Fault diagnosis
Feature extraction
Mathematical models
Mathematical optimization
Mechanical systems
Methods
Numerical analysis
Particle swarm optimization
Pattern recognition
Roller bearings
Signal to noise ratio
Support vector machines
Time domain analysis
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:As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time-domain energy entropy-based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time-domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO-SVM (particle swarm optimization-support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time-domain energy entropy and PSO-SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.
ISSN:1070-9622
1875-9203
DOI:10.1155/2021/7461402