A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM
The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the be...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9104718/ |
| _version_ | 1818428401261740032 |
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| author | Fuzheng Liu Junwei Gao Huabo Liu |
| author_facet | Fuzheng Liu Junwei Gao Huabo Liu |
| author_sort | Fuzheng Liu |
| collection | DOAJ |
| description | The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately. |
| first_indexed | 2024-12-14T15:01:02Z |
| format | Article |
| id | doaj.art-673ad79112de41d79981dde3e669569f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:01:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-673ad79112de41d79981dde3e669569f2022-12-21T22:56:50ZengIEEEIEEE Access2169-35362020-01-01810147610148810.1109/ACCESS.2020.29987229104718A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVMFuzheng Liu0https://orcid.org/0000-0002-2866-4808Junwei Gao1https://orcid.org/0000-0001-8870-2960Huabo Liu2https://orcid.org/0000-0002-4182-8934College of Automation, Qingdao University, Qingdao, ChinaCollege of Automation, Qingdao University, Qingdao, ChinaCollege of Automation, Qingdao University, Qingdao, ChinaThe vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately.https://ieeexplore.ieee.org/document/9104718/Rolling bearingMEEMDfeature extractionQPSOLSSVMintelligent fault diagnosis |
| spellingShingle | Fuzheng Liu Junwei Gao Huabo Liu A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM IEEE Access Rolling bearing MEEMD feature extraction QPSO LSSVM intelligent fault diagnosis |
| title | A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM |
| title_full | A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM |
| title_fullStr | A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM |
| title_full_unstemmed | A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM |
| title_short | A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM |
| title_sort | fault diagnosis solution of rolling bearing based on meemd and qpso lssvm |
| topic | Rolling bearing MEEMD feature extraction QPSO LSSVM intelligent fault diagnosis |
| url | https://ieeexplore.ieee.org/document/9104718/ |
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