Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering
The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MP...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/9212759 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561550776336384 |
|---|---|
| author | Tianjing He Rongzhen Zhao Yaochun Wu Chao Yang |
| author_facet | Tianjing He Rongzhen Zhao Yaochun Wu Chao Yang |
| author_sort | Tianjing He |
| collection | DOAJ |
| description | The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMFfunction is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types. |
| format | Article |
| id | doaj-art-233f627e7d52438d91f09281248345a3 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-233f627e7d52438d91f09281248345a32025-02-03T01:24:44ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/92127599212759Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG ClusteringTianjing He0Rongzhen Zhao1Yaochun Wu2Chao Yang3School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaThe nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMFfunction is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types.http://dx.doi.org/10.1155/2021/9212759 |
| spellingShingle | Tianjing He Rongzhen Zhao Yaochun Wu Chao Yang Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering Shock and Vibration |
| title | Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering |
| title_full | Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering |
| title_fullStr | Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering |
| title_full_unstemmed | Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering |
| title_short | Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering |
| title_sort | fault identification of rolling bearing using variational mode decomposition multiscale permutation entropy and adaptive gg clustering |
| url | http://dx.doi.org/10.1155/2021/9212759 |
| work_keys_str_mv | AT tianjinghe faultidentificationofrollingbearingusingvariationalmodedecompositionmultiscalepermutationentropyandadaptiveggclustering AT rongzhenzhao faultidentificationofrollingbearingusingvariationalmodedecompositionmultiscalepermutationentropyandadaptiveggclustering AT yaochunwu faultidentificationofrollingbearingusingvariationalmodedecompositionmultiscalepermutationentropyandadaptiveggclustering AT chaoyang faultidentificationofrollingbearingusingvariationalmodedecompositionmultiscalepermutationentropyandadaptiveggclustering |