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Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering
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| Zeitschriftentitel: | Advances in Mechanical Engineering |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Advances in Mechanical Engineering, 10, 2018, 10, S. 168781401880353 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
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| Schlagwörter: |
| author_facet |
Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei |
|---|---|
| author |
Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei |
| spellingShingle |
Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei Advances in Mechanical Engineering Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering Mechanical Engineering |
| author_sort |
wang, bing |
| spelling |
Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei 1687-8140 1687-8140 SAGE Publications Mechanical Engineering http://dx.doi.org/10.1177/1687814018803539 <jats:p>A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.</jats:p> Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering Advances in Mechanical Engineering |
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| title |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_unstemmed |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_full |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_fullStr |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_full_unstemmed |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_short |
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_sort |
bearing condition degradation assessment based on basic scale entropy and gath-geva fuzzy clustering |
| topic |
Mechanical Engineering |
| url |
http://dx.doi.org/10.1177/1687814018803539 |
| publishDate |
2018 |
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168781401880353 |
| description |
<jats:p>A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.</jats:p> |
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| author | Wang, Bing, Hu, Xiong, Sun, Dejian, Wang, Wei |
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| description | <jats:p>A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.</jats:p> |
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| spelling | Wang, Bing Hu, Xiong Sun, Dejian Wang, Wei 1687-8140 1687-8140 SAGE Publications Mechanical Engineering http://dx.doi.org/10.1177/1687814018803539 <jats:p>A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.</jats:p> Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering Advances in Mechanical Engineering |
| spellingShingle | Wang, Bing, Hu, Xiong, Sun, Dejian, Wang, Wei, Advances in Mechanical Engineering, Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering, Mechanical Engineering |
| title | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_full | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_fullStr | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_full_unstemmed | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_short | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| title_sort | bearing condition degradation assessment based on basic scale entropy and gath-geva fuzzy clustering |
| title_unstemmed | Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering |
| topic | Mechanical Engineering |
| url | http://dx.doi.org/10.1177/1687814018803539 |