An adaptive sampling framework for life cycle degradation monitoring
Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/169463 |
| _version_ | 1811677648789700608 |
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| author | Yin, Yuhua Liu, Zhiliang Zhang, Junhao Zio, Enrico Zuo, Mingjian |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yin, Yuhua Liu, Zhiliang Zhang, Junhao Zio, Enrico Zuo, Mingjian |
| author_sort | Yin, Yuhua |
| collection | NTU |
| description | Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring. |
| first_indexed | 2024-10-01T02:40:43Z |
| format | Journal Article |
| id | ntu-10356/169463 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:40:43Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1694632023-07-21T15:40:29Z An adaptive sampling framework for life cycle degradation monitoring Yin, Yuhua Liu, Zhiliang Zhang, Junhao Zio, Enrico Zuo, Mingjian School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Data Redundancy Data Loss Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring. Published version This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB1702400), the Sichuan Province Key Research and Development Program (Grant No. 23ZDYF0212) and the China Scholarship Council (Grant No. 202106070089). 2023-07-19T06:17:26Z 2023-07-19T06:17:26Z 2023 Journal Article Yin, Y., Liu, Z., Zhang, J., Zio, E. & Zuo, M. (2023). An adaptive sampling framework for life cycle degradation monitoring. Sensors, 23(2), 965-. https://dx.doi.org/10.3390/s23020965 1424-8220 https://hdl.handle.net/10356/169463 10.3390/s23020965 36679762 2-s2.0-85146593610 2 23 965 en Sensors © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Data Redundancy Data Loss Yin, Yuhua Liu, Zhiliang Zhang, Junhao Zio, Enrico Zuo, Mingjian An adaptive sampling framework for life cycle degradation monitoring |
| title | An adaptive sampling framework for life cycle degradation monitoring |
| title_full | An adaptive sampling framework for life cycle degradation monitoring |
| title_fullStr | An adaptive sampling framework for life cycle degradation monitoring |
| title_full_unstemmed | An adaptive sampling framework for life cycle degradation monitoring |
| title_short | An adaptive sampling framework for life cycle degradation monitoring |
| title_sort | adaptive sampling framework for life cycle degradation monitoring |
| topic | Engineering::Electrical and electronic engineering Data Redundancy Data Loss |
| url | https://hdl.handle.net/10356/169463 |
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