Application of Core Vector Regression in Condition-Based Maintenance for Electric Power Equipments

Abstract

In this paper, we propose a forecasting model of electric power equipment statement assembled by core vector machines and particle swarm algorithm to improve the accuracy of electric equipment maintenance. The electric power equipment condition forecasting model improves parameter selection problems of nuclear vector regression by particle swarm algorithm, optimizes parameters of kernel function and reduces the artificial factors in the forecasting process, accordingly reduces the blindness in the process of training and improves the accuracy of the prediction, while core vector regression have the advantages of high precision, suitable for power equipment maintenance process.

3 Figures and Tables

Cite this paper

@article{Qu2011ApplicationOC, title={Application of Core Vector Regression in Condition-Based Maintenance for Electric Power Equipments}, author={Junhua Qu and Wenjuan Wang and Chao Wei}, journal={2011 International Conference on Internet Computing and Information Services}, year={2011}, pages={539-542} }