多目标支持向量机及其在少样本故障诊断中的应用
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:


Multi-objective Support Vector Machine and Its Application in Small Sample Fault Diagnosis
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    支持向量机理论简单, 实用性很强, 被大量应用于故障诊断问题中. 在分析支持向量机参数对分类结果影响的过程发现, 不恰当的参数选择往往带来较差的分类结果. 采用启发式优化方法可以避免人为选择的不足, 但单纯以等效间隔距离为寻优目标又会较大概率出现“过学习”现象. 为降低整体结构风险, 将等效间隔距离、支持向量数量和错分率等同时作为优化目标, 提出了一种基于粒子群的多目标支持向量机方法, 并采用定时重启、动态学习因子等策略提升算法全局寻优能力. 最后将其应用于多故障强关联耦合的复杂柴油机故障诊断问题中. 实验结果表明, 该方法可以有效解决少样本、不完备或不确定征兆的柴油机异响故障诊断问题, 筛选得到的综合最优解更符合人们的期望.

    Abstract:

    Support vector machines have a simple theory and strong practicability, which are thus widely used in fault diagnosis. In the process of analyzing the influence of support vector machine parameters on classification results, it is found that inappropriate parameter selection often leads to poor classification results. The adverse effects of artificial selection can be avoided by using a heuristic optimization method. However, taking the equivalent interval distance as the optimization goal is prone to result in “over learning”. Taking the equivalent interval distance, the number of support vectors and the misclassification rate as optimization objectives at the same time, this study proposes a multi-objective support vector machine method based on particle swarm optimization. The strategies of timed restart and dynamic learning factor are used to improve the global optimization ability of the algorithm. In this way, the overall structural risk can be reduced. The proposed method is applied to the fault diagnosis of a complex diesel engine with strong correlation and coupling of multiple faults. The experimental results show that this method can effectively diagnose the fault of abnormal noise from diesel engines in the case of small samples and incomplete or uncertain symptoms, and the comprehensive optimal solution obtained by screening is more in line with people’s expectations.

    参考文献
    相似文献
    引证文献
引用本文

江勋林.多目标支持向量机及其在少样本故障诊断中的应用.计算机系统应用,,():1-7

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-12-22
  • 最后修改日期:2022-01-24
  • 录用日期:
  • 在线发布日期: 2022-06-16
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号