Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing
Many nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful tool for complexity measure of time series, a...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8675921/ |
id |
doaj-ff4503f1c3534f0a9d7c00cce05a3d38 |
---|---|
record_format |
Article |
spelling |
doaj-ff4503f1c3534f0a9d7c00cce05a3d382021-03-29T22:32:06ZengIEEEIEEE Access2169-35362019-01-017476634767310.1109/ACCESS.2019.29079978675921Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling BearingCongzhi Li0Jinde Zheng1https://orcid.org/0000-0001-5735-4916Haiyang Pan2https://orcid.org/0000-0001-9868-8154Jinyu Tong3Yifang Zhang4School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaMany nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful tool for complexity measure of time series, and compared with MSE and MFE, it gets much better performance and costs less time for computation. Since single-channel time series analysis will cause information missing, inspired by multivariate multiscale sample entropy (MMSE) and multivariate multiscale fuzzy entropy (MMFE), refined composite multivariate multiscale dispersion entropy (RCMMDE) was proposed in this paper. After that, RCMMDE was compared with MDE, MMSE, and MMFE by analyzing synthetic signals and the results show that the RCMMDE has certain advantages in terms of robustness. A hybrid fault diagnostics approach is proposed for rolling bearing with a combination of RCMMDE, multi-cluster feature selection, and support vector machine. Also, the proposed method is compared with MDE, MMSE, and MMFE, as well as multivariate multiscale dispersion entropy-based fault diagnosis methods by analyzing the experimental data of rolling bearing, and the result shows that the proposed method gets a higher identification rate than the existing other fault diagnosis methods.https://ieeexplore.ieee.org/document/8675921/Multiscale entropymultiscale fuzzy entropymultivariate multiscale dispersion entropyrefined composite multivariate multiscale dispersion entropyrolling bearingfault diagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Congzhi Li Jinde Zheng Haiyang Pan Jinyu Tong Yifang Zhang |
spellingShingle |
Congzhi Li Jinde Zheng Haiyang Pan Jinyu Tong Yifang Zhang Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing IEEE Access Multiscale entropy multiscale fuzzy entropy multivariate multiscale dispersion entropy refined composite multivariate multiscale dispersion entropy rolling bearing fault diagnosis |
author_facet |
Congzhi Li Jinde Zheng Haiyang Pan Jinyu Tong Yifang Zhang |
author_sort |
Congzhi Li |
title |
Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing |
title_short |
Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing |
title_full |
Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing |
title_fullStr |
Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing |
title_full_unstemmed |
Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing |
title_sort |
refined composite multivariate multiscale dispersion entropy and its application to fault diagnosis of rolling bearing |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Many nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful tool for complexity measure of time series, and compared with MSE and MFE, it gets much better performance and costs less time for computation. Since single-channel time series analysis will cause information missing, inspired by multivariate multiscale sample entropy (MMSE) and multivariate multiscale fuzzy entropy (MMFE), refined composite multivariate multiscale dispersion entropy (RCMMDE) was proposed in this paper. After that, RCMMDE was compared with MDE, MMSE, and MMFE by analyzing synthetic signals and the results show that the RCMMDE has certain advantages in terms of robustness. A hybrid fault diagnostics approach is proposed for rolling bearing with a combination of RCMMDE, multi-cluster feature selection, and support vector machine. Also, the proposed method is compared with MDE, MMSE, and MMFE, as well as multivariate multiscale dispersion entropy-based fault diagnosis methods by analyzing the experimental data of rolling bearing, and the result shows that the proposed method gets a higher identification rate than the existing other fault diagnosis methods. |
topic |
Multiscale entropy multiscale fuzzy entropy multivariate multiscale dispersion entropy refined composite multivariate multiscale dispersion entropy rolling bearing fault diagnosis |
url |
https://ieeexplore.ieee.org/document/8675921/ |
work_keys_str_mv |
AT congzhili refinedcompositemultivariatemultiscaledispersionentropyanditsapplicationtofaultdiagnosisofrollingbearing AT jindezheng refinedcompositemultivariatemultiscaledispersionentropyanditsapplicationtofaultdiagnosisofrollingbearing AT haiyangpan refinedcompositemultivariatemultiscaledispersionentropyanditsapplicationtofaultdiagnosisofrollingbearing AT jinyutong refinedcompositemultivariatemultiscaledispersionentropyanditsapplicationtofaultdiagnosisofrollingbearing AT yifangzhang refinedcompositemultivariatemultiscaledispersionentropyanditsapplicationtofaultdiagnosisofrollingbearing |
_version_ |
1724191456145440768 |