Summarization of Remaining Life Prediction Methods for Special Power Plants
With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/16/9365 |
| _version_ | 1797585611797823488 |
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| author | Weige Liang Chi Li Lei Zhao Xiaojia Yan Shiyan Sun |
| author_facet | Weige Liang Chi Li Lei Zhao Xiaojia Yan Shiyan Sun |
| author_sort | Weige Liang |
| collection | DOAJ |
| description | With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided. |
| first_indexed | 2024-03-11T00:08:24Z |
| format | Article |
| id | doaj.art-606ade92eec440e2a81204a874e26ecd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:08:24Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-606ade92eec440e2a81204a874e26ecd2023-11-19T00:08:38ZengMDPI AGApplied Sciences2076-34172023-08-011316936510.3390/app13169365Summarization of Remaining Life Prediction Methods for Special Power PlantsWeige Liang0Chi Li1Lei Zhao2Xiaojia Yan3Shiyan Sun4College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, ChinaWith continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.https://www.mdpi.com/2076-3417/13/16/9365remaining useful life predictionspecial power plantsdynamic modeldata-drivendata–model fusion |
| spellingShingle | Weige Liang Chi Li Lei Zhao Xiaojia Yan Shiyan Sun Summarization of Remaining Life Prediction Methods for Special Power Plants Applied Sciences remaining useful life prediction special power plants dynamic model data-driven data–model fusion |
| title | Summarization of Remaining Life Prediction Methods for Special Power Plants |
| title_full | Summarization of Remaining Life Prediction Methods for Special Power Plants |
| title_fullStr | Summarization of Remaining Life Prediction Methods for Special Power Plants |
| title_full_unstemmed | Summarization of Remaining Life Prediction Methods for Special Power Plants |
| title_short | Summarization of Remaining Life Prediction Methods for Special Power Plants |
| title_sort | summarization of remaining life prediction methods for special power plants |
| topic | remaining useful life prediction special power plants dynamic model data-driven data–model fusion |
| url | https://www.mdpi.com/2076-3417/13/16/9365 |
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