A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the p...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mingyang Lv, Chunguang Zhang, Aibin Guo, Fang Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Rolling bearing performance degradation trend prediction feature extraction principal component analysis phase space reconstruction kernel extreme learning machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3513e8f4bf5f408db8eb61ce9bb23c4c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3513e8f4bf5f408db8eb61ce9bb23c4c
record_format	dspace
spelling	oai:doaj.org-article:3513e8f4bf5f408db8eb61ce9bb23c4c2021-11-19T00:04:59ZA New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM2169-353610.1109/ACCESS.2020.3048492https://doaj.org/article/3513e8f4bf5f408db8eb61ce9bb23c4c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9311638/https://doaj.org/toc/2169-3536In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.Mingyang LvChunguang ZhangAibin GuoFang LiuIEEEarticleRolling bearingperformance degradation trend predictionfeature extractionprincipal component analysisphase space reconstructionkernel extreme learning machineElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 6188-6200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Rolling bearing performance degradation trend prediction feature extraction principal component analysis phase space reconstruction kernel extreme learning machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Rolling bearing performance degradation trend prediction feature extraction principal component analysis phase space reconstruction kernel extreme learning machine Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mingyang Lv Chunguang Zhang Aibin Guo Fang Liu A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
description	In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.
format	article
author	Mingyang Lv Chunguang Zhang Aibin Guo Fang Liu
author_facet	Mingyang Lv Chunguang Zhang Aibin Guo Fang Liu
author_sort	Mingyang Lv
title	A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
title_short	A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
title_full	A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
title_fullStr	A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
title_full_unstemmed	A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
title_sort	new performance degradation evaluation method integrating pca, psr and kelm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3513e8f4bf5f408db8eb61ce9bb23c4c
work_keys_str_mv	AT mingyanglv anewperformancedegradationevaluationmethodintegratingpcapsrandkelm AT chunguangzhang anewperformancedegradationevaluationmethodintegratingpcapsrandkelm AT aibinguo anewperformancedegradationevaluationmethodintegratingpcapsrandkelm AT fangliu anewperformancedegradationevaluationmethodintegratingpcapsrandkelm AT mingyanglv newperformancedegradationevaluationmethodintegratingpcapsrandkelm AT chunguangzhang newperformancedegradationevaluationmethodintegratingpcapsrandkelm AT aibinguo newperformancedegradationevaluationmethodintegratingpcapsrandkelm AT fangliu newperformancedegradationevaluationmethodintegratingpcapsrandkelm
_version_	1718420661694627840

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Ejemplares similares