Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis
A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2014/154291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562442755899392 |
|---|---|
| author | Jinde Zheng Junsheng Cheng Yu Yang |
| author_facet | Jinde Zheng Junsheng Cheng Yu Yang |
| author_sort | Jinde Zheng |
| collection | DOAJ |
| description | A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively. |
| format | Article |
| id | doaj-art-0b4413e2776a46408b0f94034c083e88 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-0b4413e2776a46408b0f94034c083e882025-02-03T01:22:36ZengWileyShock and Vibration1070-96221875-92032014-01-01201410.1155/2014/154291154291Multiscale Permutation Entropy Based Rolling Bearing Fault DiagnosisJinde Zheng0Junsheng Cheng1Yu Yang2State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaA new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.http://dx.doi.org/10.1155/2014/154291 |
| spellingShingle | Jinde Zheng Junsheng Cheng Yu Yang Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis Shock and Vibration |
| title | Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis |
| title_full | Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis |
| title_fullStr | Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis |
| title_full_unstemmed | Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis |
| title_short | Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis |
| title_sort | multiscale permutation entropy based rolling bearing fault diagnosis |
| url | http://dx.doi.org/10.1155/2014/154291 |
| work_keys_str_mv | AT jindezheng multiscalepermutationentropybasedrollingbearingfaultdiagnosis AT junshengcheng multiscalepermutationentropybasedrollingbearingfaultdiagnosis AT yuyang multiscalepermutationentropybasedrollingbearingfaultdiagnosis |