Dynamic Multi-Fault Diagnosis Model Based on Support Vector Domain Description

张庆 1; 徐光华 1; 王晶 1; 2; 梁霖 1; 2

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Dynamic Multi-Fault Diagnosis Model Based on Support Vector Domain Description

更新时间：2025-12-08

- Dynamic Multi-Fault Diagnosis Model Based on Support Vector Domain Description
- Vol. 41, Issue 5, Pages: 593-597(2007)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH165.3
- Online First：10 May 2007，
  
  Published：2007
- 稿件说明：
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张庆 1, 徐光华 1, 王晶 1, et al. Dynamic Multi-Fault Diagnosis Model Based on Support Vector Domain Description[J]. 2007, 41(5): 593-597.
DOI：

张庆 1, 徐光华 1, 王晶 1, et al. Dynamic Multi-Fault Diagnosis Model Based on Support Vector Domain Description[J]. 2007, 41(5): 593-597. DOI：

摘要

为了提高多故障诊断中对新故障类别和新故障数据的适应性

提出了一种新的多故障诊断动态模型.该模型采用支持向量域描述算法(SVDD)对多类故障进行单独训练

建立独立而封闭的特征空间

满足故障类别的动态增加需要

并采用样本与各特征空间的相对距离进行了多故障的混合识别.应用在线SVDD算法

在已有的故障特征分布信息基础上

通过更新操作

学习新数据信息

从而实现了故障模式的动态调整.通过仿真和机械故障实例数据的检验

表明该模型能够动态地提取多类故障的特征信息

改善诊断学习过程的适应性.

Abstract

To improve the adaptability of multi-fault diagnosis

a novel dynamic diagnosis model is proposed

which includes two main techniques: independent training and online adjusting for multi-fault modes. The data of every fault class are trained by support vector domain description(SVDD)to obtain the optimal enclosing feature spaces. Due to the independence of these distribution spaces

new classes can be generated facilely. The relative distances between fault data and the distribution spaces decide which class they belong to. Moreover

an online SVDD algorithm is developed to update the feature space

and the distribution information of new data is appended to the existing feature space timely. And the simulated and practical data are analyzed to verify the effectiveness of the model for dynamic multi-fault diagnosis.

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references

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Shen Lixiang, Francis E H, Qu Liangsheng, et al. Fault diagnosis using rough sets theory[J].Computers in Industry, 2000,43(1):61-72.

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