Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM
The rotor system is a core part of rotating machinery equipment. Its safe and reliable operation directly affects the economic benefit of using the equipment and the personal safety of users. To fully explore the complex feature mapping relationship between rotor vibration signals and fault types, r...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.944961/full |
| _version_ | 1818163779928588288 |
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| author | Lu Wang Lu Wang Lu Wang Hui Liu Hui Liu Hui Liu Jie Liang Jie Liang Jie Liang Lijuan Zhang Lijuan Zhang Lijuan Zhang Qingchang Ji Qingchang Ji Qingchang Ji Jianqiang Wang Jianqiang Wang Jianqiang Wang |
| author_facet | Lu Wang Lu Wang Lu Wang Hui Liu Hui Liu Hui Liu Jie Liang Jie Liang Jie Liang Lijuan Zhang Lijuan Zhang Lijuan Zhang Qingchang Ji Qingchang Ji Qingchang Ji Jianqiang Wang Jianqiang Wang Jianqiang Wang |
| author_sort | Lu Wang |
| collection | DOAJ |
| description | The rotor system is a core part of rotating machinery equipment. Its safe and reliable operation directly affects the economic benefit of using the equipment and the personal safety of users. To fully explore the complex feature mapping relationship between rotor vibration signals and fault types, rotor vibration signals were studied under different working conditions from the perspective of feature parameter construction and feature information mining. First, a variational mode decomposition algorithm was used to decompose the vibration signals, and quantum behavior particle swarm optimization was used to minimize the mean envelope entropy of intrinsic mode function components to determine the optimal combination of modal number and penalty coefficient. Second, the principal component analysis was used to reduce the dimensionality of IMF components of vibration signals. Finally, a support vector machine was used to mine the feature mapping relationship between vibration data after dimensionality reduction and rotor operation state to accurately identify rotor fault types. The proposed method was used to analyze the measured vibration signals of the rotor system. The experimental results showed that the proposed method effectively extracted characteristic information of the rotor running state from the vibration data, and the accuracies of four types of fault diagnoses were 100%, 88.89%, 100%, and 100%, respectively. In addition, the accuracies of the four fault diagnoses in this study were better than those of the previously reported models. |
| first_indexed | 2024-12-11T16:54:59Z |
| format | Article |
| id | doaj.art-d187ba7490a44aa7bcf91a9abd4ceb3b |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-11T16:54:59Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-d187ba7490a44aa7bcf91a9abd4ceb3b2022-12-22T00:57:59ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-07-011010.3389/fenrg.2022.944961944961Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVMLu Wang0Lu Wang1Lu Wang2Hui Liu3Hui Liu4Hui Liu5Jie Liang6Jie Liang7Jie Liang8Lijuan Zhang9Lijuan Zhang10Lijuan Zhang11Qingchang Ji12Qingchang Ji13Qingchang Ji14Jianqiang Wang15Jianqiang Wang16Jianqiang Wang17Hebei University of Water Resources and Electric Engineering, Cangzhou, ChinaWater Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaHebei Industrial Manipulator Control and Reliability Technology Innovation Center, Cangzhou, ChinaCangzhou Industrial Manipulator Control and Reliability Technology Innovation Center, Cangzhou, ChinaHebei University of Water Resources and Electric Engineering, Cangzhou, ChinaWater Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaHebei University of Water Resources and Electric Engineering, Cangzhou, ChinaWater Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaHebei University of Water Resources and Electric Engineering, Cangzhou, ChinaWater Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaHebei University of Water Resources and Electric Engineering, Cangzhou, ChinaWater Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou, ChinaDepartment of Mechanical Engineering, Hebei Institute of Water Conservancy and Electric Power, Cangzhou, ChinaThe rotor system is a core part of rotating machinery equipment. Its safe and reliable operation directly affects the economic benefit of using the equipment and the personal safety of users. To fully explore the complex feature mapping relationship between rotor vibration signals and fault types, rotor vibration signals were studied under different working conditions from the perspective of feature parameter construction and feature information mining. First, a variational mode decomposition algorithm was used to decompose the vibration signals, and quantum behavior particle swarm optimization was used to minimize the mean envelope entropy of intrinsic mode function components to determine the optimal combination of modal number and penalty coefficient. Second, the principal component analysis was used to reduce the dimensionality of IMF components of vibration signals. Finally, a support vector machine was used to mine the feature mapping relationship between vibration data after dimensionality reduction and rotor operation state to accurately identify rotor fault types. The proposed method was used to analyze the measured vibration signals of the rotor system. The experimental results showed that the proposed method effectively extracted characteristic information of the rotor running state from the vibration data, and the accuracies of four types of fault diagnoses were 100%, 88.89%, 100%, and 100%, respectively. In addition, the accuracies of the four fault diagnoses in this study were better than those of the previously reported models.https://www.frontiersin.org/articles/10.3389/fenrg.2022.944961/fullrotor fault diagnosissupport vector machineVMDQPSOPCA |
| spellingShingle | Lu Wang Lu Wang Lu Wang Hui Liu Hui Liu Hui Liu Jie Liang Jie Liang Jie Liang Lijuan Zhang Lijuan Zhang Lijuan Zhang Qingchang Ji Qingchang Ji Qingchang Ji Jianqiang Wang Jianqiang Wang Jianqiang Wang Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM Frontiers in Energy Research rotor fault diagnosis support vector machine VMD QPSO PCA |
| title | Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM |
| title_full | Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM |
| title_fullStr | Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM |
| title_full_unstemmed | Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM |
| title_short | Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM |
| title_sort | research on the rotor fault diagnosis method based on qpso vmd pca svm |
| topic | rotor fault diagnosis support vector machine VMD QPSO PCA |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.944961/full |
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