基于时序AR与灰色GM模型的滚动轴承故障诊断研究
Aiming at the problems of roller bearing fault diagnosis,gray theory and auto-regressive combination forecasting model is put forward,and the combination model has been build. The methodology developed decomposes the signal in intrinsic oscillation modes first,to translate the non-stationary signals...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2009-01-01
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| Series: | Jixie chuandong |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2009.06.012 |
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| _version_ | 1849734306036973568 |
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| author | 陈瑞华 杨宗伟 |
| author_facet | 陈瑞华 杨宗伟 |
| author_sort | 陈瑞华 |
| collection | DOAJ |
| description | Aiming at the problems of roller bearing fault diagnosis,gray theory and auto-regressive combination forecasting model is put forward,and the combination model has been build. The methodology developed decomposes the signal in intrinsic oscillation modes first,to translate the non-stationary signals into stationary signals. Then the autoregressive （AR） model of the selected IMF is established. The rough trend of the wear particle content change can be reflected through gray theory,and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of graphic data,the result shows that the combination model has a better forecasting result. |
| format | Article |
| id | doaj-art-8145fbed9de84a2aa8a24e063a210a5d |
| institution | DOAJ |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2009-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-8145fbed9de84a2aa8a24e063a210a5d2025-08-20T03:07:50ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392009-01-01338991+97+12388665071基于时序AR与灰色GM模型的滚动轴承故障诊断研究陈瑞华杨宗伟Aiming at the problems of roller bearing fault diagnosis,gray theory and auto-regressive combination forecasting model is put forward,and the combination model has been build. The methodology developed decomposes the signal in intrinsic oscillation modes first,to translate the non-stationary signals into stationary signals. Then the autoregressive （AR） model of the selected IMF is established. The rough trend of the wear particle content change can be reflected through gray theory,and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of graphic data,the result shows that the combination model has a better forecasting result.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2009.06.012 |
| spellingShingle | 陈瑞华 杨宗伟 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 Jixie chuandong |
| title | 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 |
| title_full | 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 |
| title_fullStr | 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 |
| title_full_unstemmed | 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 |
| title_short | 基于时序AR与灰色GM模型的滚动轴承故障诊断研究 |
| title_sort | 基于时序ar与灰色gm模型的滚动轴承故障诊断研究 |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2009.06.012 |
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