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
Hauptverfasser: Jia, Hongxia, Liu, Haiwei, Yang, Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Coastal Education and Research Foundation 01.12.2015
Coastal Education & Research Foundation (CERF)
Schlagworte:
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
ISSN:0749-0208, 1551-5036
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Zusammenfassung: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.
ISSN:0749-0208
1551-5036
DOI:10.2112/SI73-025.1