Research on the Prediction of Health Status of the Container Gantry Crane Energy Systems
Jia, H.; Liu, H., and Yang, Y., 2015. The research on the prediction of health status of the container gantry crane energy systems. In this paper, the remaining capacity of lead-acid batteries is used to evaluate the health status of RTG energy systems. A LS-SVM model was established for predicting...
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| Veröffentlicht in: | Journal of coastal research Jg. 73; H. sp1; S. 139 - 145 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Coastal Education and Research Foundation
01.12.2015
Coastal Education & Research Foundation (CERF) |
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| ISSN: | 0749-0208, 1551-5036 |
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| Abstract | Jia, H.; Liu, H., and Yang, Y., 2015. The research on the prediction of health status of the container gantry crane energy systems. In this paper, the remaining capacity of lead-acid batteries is used to evaluate the health status of RTG energy systems. A LS-SVM model was established for predicting the remaining capacity of batteries, with the PSO-BP algorithm optimizing the parameters in the LS-SVM model. Using the trained LS-SVM model, the remaining capacity of batteries and the degradation trend of battery capacity with time are predicted. Compared with measured results, the predicted results show that the LS-SVM model can accurately predict the remaining capacity of lead-acid batteries. |
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| AbstractList | Jia, H.; Liu, H., and Yang, Y., 2015. The research on the prediction of health status of the container gantry crane energy systems. In this paper, the remaining capacity of lead-acid batteries is used to evaluate the health status of RTG energy systems. A LS-SVM model was established for predicting the remaining capacity of batteries, with the PSO-BP algorithm optimizing the parameters in the LS-SVM model. Using the trained LS-SVM model, the remaining capacity of batteries and the degradation trend of battery capacity with time are predicted. Compared with measured results, the predicted results show that the LS-SVM model can accurately predict the remaining capacity of lead-acid batteries. In this paper, the remaining capacity of lead-acid batteries is used to evaluate the health status of RTG energy systems. A LS-SVM model was established for predicting the remaining capacity of batteries, with the PSO-BP algorithm optimizing the parameters in the LS-SVM model. Using the trained LS-SVM model, the remaining capacity of batteries and the degradation trend of battery capacity with time are predicted. Compared with measured results, the predicted results show that the LS-SVM model can accurately predict the remaining capacity of lead-acid batteries. |
| Author | Liu, Haiwei Yang, Yang Jia, Hongxia |
| Author_xml | – sequence: 1 givenname: Hongxia surname: Jia fullname: Jia, Hongxia organization: Logistics Engineering College, Shanghai maritime University, Shanghai 201306, China – sequence: 2 givenname: Haiwei surname: Liu fullname: Liu, Haiwei organization: Logistics Engineering College, Shanghai maritime University, Shanghai 201306, China – sequence: 3 givenname: Yang surname: Yang fullname: Yang, Yang organization: Logistics Engineering College, Shanghai maritime University, Shanghai 201306, China |
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| Copyright | 2015 Coastal Education and Research Foundation 2014 Coastal Education & Research Foundation (CERF) |
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| Snippet | Jia, H.; Liu, H., and Yang, Y., 2015. The research on the prediction of health status of the container gantry crane energy systems. In this paper, the... In this paper, the remaining capacity of lead-acid batteries is used to evaluate the health status of RTG energy systems. A LS-SVM model was established for... |
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| StartPage | 139 |
| SubjectTerms | Artificial neural networks Batteries Energy energy systems Error rates Gantry cranes Health status ls-svm model Marine Resources and Biodiversity Mathematical independent variables Mathematical vectors Modeling Parametric models pso-bp algorithm The container gantry crane the prediction of the health status |
| Title | Research on the Prediction of Health Status of the Container Gantry Crane Energy Systems |
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