Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service
A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to charac...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/7396293 |
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| _version_ | 1832567981519929344 |
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| author | Liang Ye Xintao Xia Zhen Chang |
| author_facet | Liang Ye Xintao Xia Zhen Chang |
| author_sort | Liang Ye |
| collection | DOAJ |
| description | A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails. |
| format | Article |
| id | doaj-art-05b8ab1e93b046b79cccf86b466aa6b1 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-05b8ab1e93b046b79cccf86b466aa6b12025-02-03T00:59:58ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/73962937396293Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in ServiceLiang Ye0Xintao Xia1Zhen Chang2School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaMechatronical Engineering College, Henan University of Science and Technology, Luoyang 471003, ChinaHangzhou Bearing Test & Research Center, Hangzhou 310022, ChinaA dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails.http://dx.doi.org/10.1155/2018/7396293 |
| spellingShingle | Liang Ye Xintao Xia Zhen Chang Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service Shock and Vibration |
| title | Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service |
| title_full | Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service |
| title_fullStr | Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service |
| title_full_unstemmed | Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service |
| title_short | Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service |
| title_sort | dynamic prediction for accuracy maintaining reliability of superprecision rolling bearing in service |
| url | http://dx.doi.org/10.1155/2018/7396293 |
| work_keys_str_mv | AT liangye dynamicpredictionforaccuracymaintainingreliabilityofsuperprecisionrollingbearinginservice AT xintaoxia dynamicpredictionforaccuracymaintainingreliabilityofsuperprecisionrollingbearinginservice AT zhenchang dynamicpredictionforaccuracymaintainingreliabilityofsuperprecisionrollingbearinginservice |