Cargando…
Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index
Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to r...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142948/ https://www.ncbi.nlm.nih.gov/pubmed/35632298 http://dx.doi.org/10.3390/s22103889 |
_version_ | 1784715685103927296 |
---|---|
author | Guo, Yuanjing Yang, Youdong Jiang, Shaofei Jin, Xiaohang Wei, Yanding |
author_facet | Guo, Yuanjing Yang, Youdong Jiang, Shaofei Jin, Xiaohang Wei, Yanding |
author_sort | Guo, Yuanjing |
collection | PubMed |
description | Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method. |
format | Online Article Text |
id | pubmed-9142948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91429482022-05-29 Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index Guo, Yuanjing Yang, Youdong Jiang, Shaofei Jin, Xiaohang Wei, Yanding Sensors (Basel) Article Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method. MDPI 2022-05-20 /pmc/articles/PMC9142948/ /pubmed/35632298 http://dx.doi.org/10.3390/s22103889 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Yuanjing Yang, Youdong Jiang, Shaofei Jin, Xiaohang Wei, Yanding Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title | Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title_full | Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title_fullStr | Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title_short | Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index |
title_sort | rolling bearing fault diagnosis based on successive variational mode decomposition and the ep index |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142948/ https://www.ncbi.nlm.nih.gov/pubmed/35632298 http://dx.doi.org/10.3390/s22103889 |
work_keys_str_mv | AT guoyuanjing rollingbearingfaultdiagnosisbasedonsuccessivevariationalmodedecompositionandtheepindex AT yangyoudong rollingbearingfaultdiagnosisbasedonsuccessivevariationalmodedecompositionandtheepindex AT jiangshaofei rollingbearingfaultdiagnosisbasedonsuccessivevariationalmodedecompositionandtheepindex AT jinxiaohang rollingbearingfaultdiagnosisbasedonsuccessivevariationalmodedecompositionandtheepindex AT weiyanding rollingbearingfaultdiagnosisbasedonsuccessivevariationalmodedecompositionandtheepindex |