Bearing Data Model of Correlation Probability Box Based on New G-Copula Function
Bearing failure often occurs in rotating machinery. Fault diagnosis method based on vibration signals has been studied for many years. Considering complementary information of the vibration signals from different directions, this article proposed an applied model of a correlation probability box bas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9259044/ |
| _version_ | 1818853254255083520 |
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| author | Liangcai Dong Ying Liu Hong Tang Yi Du |
| author_facet | Liangcai Dong Ying Liu Hong Tang Yi Du |
| author_sort | Liangcai Dong |
| collection | DOAJ |
| description | Bearing failure often occurs in rotating machinery. Fault diagnosis method based on vibration signals has been studied for many years. Considering complementary information of the vibration signals from different directions, this article proposed an applied model of a correlation probability box based on G-Copula function for diagnosing bearing faults. First, to avoid constructing binary Copula function directly from the definition of binary Copula function, a new function is defined, and a construction method of binary G-Copula function is proposed based on the new function. Then, the correlation probability box model is established based on a joint cumulative distribution of the G-Copula function to increase the independent of the input data in the support vector machine (SVM) model, and the aggregated widths of the correlation probability box model can be used to monitor a development of the bearing failure. Finally, the experimental results showed that the proposed method obtain the better classification accuracy than other data processing study. |
| first_indexed | 2024-12-19T07:33:53Z |
| format | Article |
| id | doaj.art-d1d770af4ce446e0af4e521163cc3a48 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:33:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1d770af4ce446e0af4e521163cc3a482022-12-21T20:30:38ZengIEEEIEEE Access2169-35362020-01-01822456522457710.1109/ACCESS.2020.30379759259044Bearing Data Model of Correlation Probability Box Based on New G-Copula FunctionLiangcai Dong0Ying Liu1Hong Tang2https://orcid.org/0000-0002-4378-1463Yi Du3https://orcid.org/0000-0001-8774-005XDepartment of Industrial Engineering, Shanghai Maritime University, Shanghai, ChinaDepartment of Industrial Engineering, Shanghai Maritime University, Shanghai, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, ChinaCity College, Kunming University of Science and Technology, Kunming, ChinaBearing failure often occurs in rotating machinery. Fault diagnosis method based on vibration signals has been studied for many years. Considering complementary information of the vibration signals from different directions, this article proposed an applied model of a correlation probability box based on G-Copula function for diagnosing bearing faults. First, to avoid constructing binary Copula function directly from the definition of binary Copula function, a new function is defined, and a construction method of binary G-Copula function is proposed based on the new function. Then, the correlation probability box model is established based on a joint cumulative distribution of the G-Copula function to increase the independent of the input data in the support vector machine (SVM) model, and the aggregated widths of the correlation probability box model can be used to monitor a development of the bearing failure. Finally, the experimental results showed that the proposed method obtain the better classification accuracy than other data processing study.https://ieeexplore.ieee.org/document/9259044/Bearing failurecopula functioncorrelation probability boxsupport vector machineclassification |
| spellingShingle | Liangcai Dong Ying Liu Hong Tang Yi Du Bearing Data Model of Correlation Probability Box Based on New G-Copula Function IEEE Access Bearing failure copula function correlation probability box support vector machine classification |
| title | Bearing Data Model of Correlation Probability Box Based on New G-Copula Function |
| title_full | Bearing Data Model of Correlation Probability Box Based on New G-Copula Function |
| title_fullStr | Bearing Data Model of Correlation Probability Box Based on New G-Copula Function |
| title_full_unstemmed | Bearing Data Model of Correlation Probability Box Based on New G-Copula Function |
| title_short | Bearing Data Model of Correlation Probability Box Based on New G-Copula Function |
| title_sort | bearing data model of correlation probability box based on new g copula function |
| topic | Bearing failure copula function correlation probability box support vector machine classification |
| url | https://ieeexplore.ieee.org/document/9259044/ |
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