A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes
Traditional similarity-based methods generally ignore the diversity of equipment fault modes, the difference in degradation rates, and the inconsistency among monitoring data lengths. Thus, a similarity-based multi-scale ensemble method in multiple fault modes (MFM-MSEN) is proposed to improve remai...
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| Формат: | Статья |
| Язык: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2022-05-01
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| Серии: | Shanghai Jiaotong Daxue xuebao |
| Предметы: | |
| Online-ссылка: | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-5-564.shtml |
| _version_ | 1828216877853179904 |
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| author | SHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng |
| author_facet | SHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng |
| author_sort | SHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng |
| collection | DOAJ |
| description | Traditional similarity-based methods generally ignore the diversity of equipment fault modes, the difference in degradation rates, and the inconsistency among monitoring data lengths. Thus, a similarity-based multi-scale ensemble method in multiple fault modes (MFM-MSEN) is proposed to improve remaining useful life (RUL) prediction accuracy and characterize prediction uncertainty. By training the fault mode classification model, designing the time-series weighted prediction strategy, and recognizing the fault mode of equipment, the test equipment is matched with the training equipment with the same fault mode to reduce matching complexity, based on which, a multi-scale ensemble strategy is proposed to overcome the data utilization limitation caused by single-scale matching methods and enhance the generalization ability of the proposed MFM-MSEN method. This strategy matches the similarities between test equipment and training equipment at multiple time scales, and then multiscale prediction results are integrated to fit accurate RUL probability distribution by employing kernel density estimation. Experimental results demonstrate the superiority of the proposed MFM-MSEN method in dealing with the differences in equipment degradation. |
| first_indexed | 2024-04-12T15:39:34Z |
| format | Article |
| id | doaj.art-03d8f9e45759495e901b25aa93fd64cb |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-04-12T15:39:34Z |
| publishDate | 2022-05-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-03d8f9e45759495e901b25aa93fd64cb2022-12-22T03:26:51ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672022-05-0156556457510.16183/j.cnki.jsjtu.2021.024A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault ModesSHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng0State Key Laboratory of Mechanical System and Vibration;School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaTraditional similarity-based methods generally ignore the diversity of equipment fault modes, the difference in degradation rates, and the inconsistency among monitoring data lengths. Thus, a similarity-based multi-scale ensemble method in multiple fault modes (MFM-MSEN) is proposed to improve remaining useful life (RUL) prediction accuracy and characterize prediction uncertainty. By training the fault mode classification model, designing the time-series weighted prediction strategy, and recognizing the fault mode of equipment, the test equipment is matched with the training equipment with the same fault mode to reduce matching complexity, based on which, a multi-scale ensemble strategy is proposed to overcome the data utilization limitation caused by single-scale matching methods and enhance the generalization ability of the proposed MFM-MSEN method. This strategy matches the similarities between test equipment and training equipment at multiple time scales, and then multiscale prediction results are integrated to fit accurate RUL probability distribution by employing kernel density estimation. Experimental results demonstrate the superiority of the proposed MFM-MSEN method in dealing with the differences in equipment degradation.http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-5-564.shtmlremaining useful life (rul)fault mode identificationmultiscale ensemble strategykernel density estimationsimilarity-based method |
| spellingShingle | SHU Junqing, XU Yuhui, XIA Tangbin, PAN Ershun, XI Lifeng A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes Shanghai Jiaotong Daxue xuebao remaining useful life (rul) fault mode identification multiscale ensemble strategy kernel density estimation similarity-based method |
| title | A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes |
| title_full | A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes |
| title_fullStr | A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes |
| title_full_unstemmed | A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes |
| title_short | A Multiscale Similarity Ensemble Methodology for Remaining Useful Life Prediction in Multiple Fault Modes |
| title_sort | multiscale similarity ensemble methodology for remaining useful life prediction in multiple fault modes |
| topic | remaining useful life (rul) fault mode identification multiscale ensemble strategy kernel density estimation similarity-based method |
| url | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-5-564.shtml |
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