Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART
In order to improve the accuracy and stability of rolling bearing performance prediction,a prediction method combining soft predictive ART-RBF integrated forecasting model and confidence CV value is proposed. The soft ART is introduced into the RBF neural network to establish the soft ART-RBF neural...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.01.028 |
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| _version_ | 1841547409328439296 |
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| author | Zhao Qiankun Wan Xiaojin Xu Zengbing Wang Kai Li Qinglei |
| author_facet | Zhao Qiankun Wan Xiaojin Xu Zengbing Wang Kai Li Qinglei |
| author_sort | Zhao Qiankun |
| collection | DOAJ |
| description | In order to improve the accuracy and stability of rolling bearing performance prediction,a prediction method combining soft predictive ART-RBF integrated forecasting model and confidence CV value is proposed. The soft ART is introduced into the RBF neural network to establish the soft ART-RBF neural network prediction model. Combining with weighted average technology,the establishment of integrated soft ART-RBF neural network prediction model is carried out. And the confidence degree（CV） value with rich fault information is obtained through the self-organizing map（SOM） network as a comprehensive index to characterize the degradation of rolling bearing performance. Finally,the above method is verified by the acceleration signal obtained by the accelerated fatigue test of the rolling bearing. The results show that the method can effectively improve the accuracy and stability of the prediction of the degradation trend of rolling bearings. |
| format | Article |
| id | doaj-art-4d3bdb5c40454bc49ae6820963dfd8cc |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-4d3bdb5c40454bc49ae6820963dfd8cc2025-01-10T14:43:44ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014213113629934088Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ARTZhao QiankunWan XiaojinXu ZengbingWang KaiLi QingleiIn order to improve the accuracy and stability of rolling bearing performance prediction,a prediction method combining soft predictive ART-RBF integrated forecasting model and confidence CV value is proposed. The soft ART is introduced into the RBF neural network to establish the soft ART-RBF neural network prediction model. Combining with weighted average technology,the establishment of integrated soft ART-RBF neural network prediction model is carried out. And the confidence degree（CV） value with rich fault information is obtained through the self-organizing map（SOM） network as a comprehensive index to characterize the degradation of rolling bearing performance. Finally,the above method is verified by the acceleration signal obtained by the accelerated fatigue test of the rolling bearing. The results show that the method can effectively improve the accuracy and stability of the prediction of the degradation trend of rolling bearings.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.01.028Soft ART-RBFSelf-organizing mapping networkConfidence valueRolling bearingPrediction |
| spellingShingle | Zhao Qiankun Wan Xiaojin Xu Zengbing Wang Kai Li Qinglei Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART Jixie chuandong Soft ART-RBF Self-organizing mapping network Confidence value Rolling bearing Prediction |
| title | Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART |
| title_full | Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART |
| title_fullStr | Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART |
| title_full_unstemmed | Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART |
| title_short | Regression Trend Prediction of Rolling Bearing Performance based on Integrated Soft Competition ART |
| title_sort | regression trend prediction of rolling bearing performance based on integrated soft competition art |
| topic | Soft ART-RBF Self-organizing mapping network Confidence value Rolling bearing Prediction |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.01.028 |
| work_keys_str_mv | AT zhaoqiankun regressiontrendpredictionofrollingbearingperformancebasedonintegratedsoftcompetitionart AT wanxiaojin regressiontrendpredictionofrollingbearingperformancebasedonintegratedsoftcompetitionart AT xuzengbing regressiontrendpredictionofrollingbearingperformancebasedonintegratedsoftcompetitionart AT wangkai regressiontrendpredictionofrollingbearingperformancebasedonintegratedsoftcompetitionart AT liqinglei regressiontrendpredictionofrollingbearingperformancebasedonintegratedsoftcompetitionart |