Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM
The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the origi...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/6/762 |
| _version_ | 1827689601516437504 |
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| author | Maoyou Ye Xiaoan Yan Minping Jia |
| author_facet | Maoyou Ye Xiaoan Yan Minping Jia |
| author_sort | Maoyou Ye |
| collection | DOAJ |
| description | The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)). |
| first_indexed | 2024-03-10T10:20:56Z |
| format | Article |
| id | doaj.art-df67d815cb874f62a9c06c0e2462a9c3 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T10:20:56Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-df67d815cb874f62a9c06c0e2462a9c32023-11-22T00:26:06ZengMDPI AGEntropy1099-43002021-06-0123676210.3390/e23060762Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVMMaoyou Ye0Xiaoan Yan1Minping Jia2School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechanical Engineering, Southeast University, Nanjing 211189, ChinaThe goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).https://www.mdpi.com/1099-4300/23/6/762variational modal decompositionmultiscale permutation entropyparticle swarm optimization-based support vector machinerolling bearingfault diagnosis |
| spellingShingle | Maoyou Ye Xiaoan Yan Minping Jia Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM Entropy variational modal decomposition multiscale permutation entropy particle swarm optimization-based support vector machine rolling bearing fault diagnosis |
| title | Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM |
| title_full | Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM |
| title_short | Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM |
| title_sort | rolling bearing fault diagnosis based on vmd mpe and pso svm |
| topic | variational modal decomposition multiscale permutation entropy particle swarm optimization-based support vector machine rolling bearing fault diagnosis |
| url | https://www.mdpi.com/1099-4300/23/6/762 |
| work_keys_str_mv | AT maoyouye rollingbearingfaultdiagnosisbasedonvmdmpeandpsosvm AT xiaoanyan rollingbearingfaultdiagnosisbasedonvmdmpeandpsosvm AT minpingjia rollingbearingfaultdiagnosisbasedonvmdmpeandpsosvm |