Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge
Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4375690 |
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| author | Yong Yu Changhua Hu Xiaosheng Si Jianxun Zhang |
| author_facet | Yong Yu Changhua Hu Xiaosheng Si Jianxun Zhang |
| author_sort | Yong Yu |
| collection | DOAJ |
| description | Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments. |
| format | Article |
| id | doaj-art-941d7f37e9c24636a9ded2cec6b1742a |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-941d7f37e9c24636a9ded2cec6b1742a2025-02-03T01:12:21ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/43756904375690Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation KnowledgeYong Yu0Changhua Hu1Xiaosheng Si2Jianxun Zhang3Department of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi 710025, ChinaDepartment of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi 710025, ChinaDepartment of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi 710025, ChinaDepartment of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi 710025, ChinaRecent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments.http://dx.doi.org/10.1155/2017/4375690 |
| spellingShingle | Yong Yu Changhua Hu Xiaosheng Si Jianxun Zhang Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge Journal of Control Science and Engineering |
| title | Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge |
| title_full | Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge |
| title_fullStr | Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge |
| title_full_unstemmed | Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge |
| title_short | Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge |
| title_sort | degradation data driven remaining useful life estimation in the absence of prior degradation knowledge |
| url | http://dx.doi.org/10.1155/2017/4375690 |
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