A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component
Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, mach...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2017-01-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814016685963 |
| _version_ | 1818996053935915008 |
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| author | Fuqiang Sun Xiaoyang Li Haitao Liao Xiankun Zhang |
| author_facet | Fuqiang Sun Xiaoyang Li Haitao Liao Xiankun Zhang |
| author_sort | Fuqiang Sun |
| collection | DOAJ |
| description | Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components. |
| first_indexed | 2024-12-20T21:23:38Z |
| format | Article |
| id | doaj.art-21dc6272b24b401dac7be10c7bb7afb3 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-20T21:23:38Z |
| publishDate | 2017-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-21dc6272b24b401dac7be10c7bb7afb32022-12-21T19:26:12ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-01-01910.1177/168781401668596310.1177_1687814016685963A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave componentFuqiang Sun0Xiaoyang Li1Haitao Liao2Xiankun Zhang3Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USAScience and Technology on Reliability and Environmental Engineering Laboratory, School of Reliability and Systems Engineering, Beihang University, Beijing, ChinaDepartment of Industrial Engineering, University of Arkansas, Fayetteville, AR, USAScience and Technology on Reliability and Environmental Engineering Laboratory, School of Reliability and Systems Engineering, Beihang University, Beijing, ChinaRapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.https://doi.org/10.1177/1687814016685963 |
| spellingShingle | Fuqiang Sun Xiaoyang Li Haitao Liao Xiankun Zhang A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component Advances in Mechanical Engineering |
| title | A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component |
| title_full | A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component |
| title_fullStr | A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component |
| title_full_unstemmed | A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component |
| title_short | A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component |
| title_sort | bayesian least squares support vector machine method for predicting the remaining useful life of a microwave component |
| url | https://doi.org/10.1177/1687814016685963 |
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