REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE
Due to the limited bearing degradation data under actual working conditions,it is impossible to obtain enough degradation data to train the neural network,it is difficult to obtain good prediction results in the deep learning network,so a new fusion method was proposed.Firstly,the features of the or...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-08-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.028 |
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| _version_ | 1841534138126958592 |
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| author | ZHONG JianHua HUANG Cong ZHONG ShunCong XIAO ShunGen |
| author_facet | ZHONG JianHua HUANG Cong ZHONG ShunCong XIAO ShunGen |
| author_sort | ZHONG JianHua |
| collection | DOAJ |
| description | Due to the limited bearing degradation data under actual working conditions,it is impossible to obtain enough degradation data to train the neural network,it is difficult to obtain good prediction results in the deep learning network,so a new fusion method was proposed.Firstly,the features of the original vibration signal was extracted,dozens of dimensional features were obtained through the ensemble empirical mode decomposition(EEMD)and the singular value decomposition(SVD),and the effective features such as kurtosis and mean value commonly used in remaining useful life prediction were added,then the decision tree to filter out 15⁃dimensional features was used the data was obtained by double exponential model fitting and the degraded signal was reduced to a linear trend through t⁃SNE.The linear degradation trend has better generalization in prediction than the exponential trend,and the prediction accuracy is superior to support veotor regression(SVR)and deep belief network(DBN)model. |
| format | Article |
| id | doaj-art-ff7e0c70a66141e5a59dabe3b26517d1 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-ff7e0c70a66141e5a59dabe3b26517d12025-01-15T02:46:05ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-08-014696997679314540REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNEZHONG JianHuaHUANG CongZHONG ShunCongXIAO ShunGenDue to the limited bearing degradation data under actual working conditions,it is impossible to obtain enough degradation data to train the neural network,it is difficult to obtain good prediction results in the deep learning network,so a new fusion method was proposed.Firstly,the features of the original vibration signal was extracted,dozens of dimensional features were obtained through the ensemble empirical mode decomposition(EEMD)and the singular value decomposition(SVD),and the effective features such as kurtosis and mean value commonly used in remaining useful life prediction were added,then the decision tree to filter out 15⁃dimensional features was used the data was obtained by double exponential model fitting and the degraded signal was reduced to a linear trend through t⁃SNE.The linear degradation trend has better generalization in prediction than the exponential trend,and the prediction accuracy is superior to support veotor regression(SVR)and deep belief network(DBN)model.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.028Feature extractionBearingRemaining useful life predictionDouble exponential modelt-distributed stochastic neighbor embedding |
| spellingShingle | ZHONG JianHua HUANG Cong ZHONG ShunCong XIAO ShunGen REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE Jixie qiangdu Feature extraction Bearing Remaining useful life prediction Double exponential model t-distributed stochastic neighbor embedding |
| title | REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE |
| title_full | REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE |
| title_fullStr | REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE |
| title_full_unstemmed | REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE |
| title_short | REMAINING USEFUL LIFE OF ROLLING BEARING BASED ON t⁃SNE |
| title_sort | remaining useful life of rolling bearing based on t⁃sne |
| topic | Feature extraction Bearing Remaining useful life prediction Double exponential model t-distributed stochastic neighbor embedding |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.028 |
| work_keys_str_mv | AT zhongjianhua remainingusefullifeofrollingbearingbasedontsne AT huangcong remainingusefullifeofrollingbearingbasedontsne AT zhongshuncong remainingusefullifeofrollingbearingbasedontsne AT xiaoshungen remainingusefullifeofrollingbearingbasedontsne |