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Fault Forecast of Electronic Equipment Based on ε -SVR

机译:基于ε-SVR的电子设备故障预测。

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摘要

In order to ensure security and reliability of the equipment, so as to decrease the maintenance cost, combining with the characteristics of fault data, this paper adopts ε - support vector regression to establish a fault forecast model and evaluation system to prediction model effect which are proper to the electronic equipment. Selecting multi-electronic equipment and training on the ε - SVR with different kernel functions. It is demonstrated that the prediction effect is better and it is still of vital realistic significance for realizing condition-based maintenance of modern electronic equipment.
机译:为了保证设备的安全性和可靠性,降低维护成本,结合故障数据的特点,采用ε-支持向量回归建立故障预测模型和评估系统,以预测模型的效果。适用于电子设备。选择多电子设备并在具有不同内核功能的ε-SVR上进行培训。结果表明,预测效果较好,对于实现基于条件的现代电子设备维护仍具有重要的现实意义。

著录项

  • 来源
  • 会议地点 Chengdu(CN)
  • 作者

    Lina Liu; Jihong Shen; Hui Zhao;

  • 作者单位

    College of Automation, Harbin Engineering University, 150001, Harbin, Heilongjiang Province, China;

    College of Automation, Harbin Engineering University, 150001, Harbin, Heilongjiang Province, China;

    Department of Mathematics, Heilongjiang Institute of Technology, 150050, Harbin, Heilongjiang Province, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data Mining; ε -SVR; Kernel Function; Fault Forecast;

    机译:数据挖掘; ε-SVR;内核功能;故障预测;

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