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基于PCA和WPSVM的航天器电特性识别方法

李可 刘祎 杜少毅 孙毅 王浚

李可, 刘祎, 杜少毅, 等 . 基于PCA和WPSVM的航天器电特性识别方法[J]. 北京航空航天大学学报, 2015, 41(7): 1177-1182. doi: 10.13700/j.bh.1001-5965.2014.0482
引用本文: 李可, 刘祎, 杜少毅, 等 . 基于PCA和WPSVM的航天器电特性识别方法[J]. 北京航空航天大学学报, 2015, 41(7): 1177-1182. doi: 10.13700/j.bh.1001-5965.2014.0482
LI Ke, LIU Yi, DU Shaoyi, et al. Spacecraft electrical characteristics identification method based on PCA feature extraction and WPSVM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(7): 1177-1182. doi: 10.13700/j.bh.1001-5965.2014.0482(in Chinese)
Citation: LI Ke, LIU Yi, DU Shaoyi, et al. Spacecraft electrical characteristics identification method based on PCA feature extraction and WPSVM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(7): 1177-1182. doi: 10.13700/j.bh.1001-5965.2014.0482(in Chinese)

基于PCA和WPSVM的航天器电特性识别方法

doi: 10.13700/j.bh.1001-5965.2014.0482
基金项目: 航空科学基金(2012XX1043); 中央高校基本科研业务费专项资金(YWF-14-HKXY-017)
详细信息
    通讯作者:

    李可(1980—),男,江苏徐州人,讲师,like@buaa.edu.cn,主要研究方向为智能控制、机器学习等.

  • 中图分类号: TP391.4

Spacecraft electrical characteristics identification method based on PCA feature extraction and WPSVM

  • 摘要: 针对航天器电特性监测系统识别过程中存在测试数据量大、特征维数高、样本少、计算速度慢和识别率低等问题,提出基于主成分分析(PCA)的特征提取和加权近似支持向量机(WPSVM)的在线故障诊断方法.实现了对信号故障特征的主成分分析、选择和提取,并对高维特征数据实现了降维,提高了航天器电特性在线故障诊断的准确性和速度.针对PCA中的结果选取问题,提出运用数据贡献度阈值进行数据截取的方法,有效地保证了数据的有效性与一致性.结果表明:该方法充分利用了航天器电特性监测系统的有用数据特征,有效提高了识别的精度,且计算时间较短,效率较高.

     

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出版历程
  • 收稿日期:  2014-07-31
  • 修回日期:  2014-09-26
  • 刊出日期:  2015-07-20

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