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计算机工程 ›› 2012, Vol. 38 ›› Issue (04): 170-173. doi: 10.3969/j.issn.1000-3428.2012.04.055

• 人工智能及识别技术 • 上一篇    下一篇

结合OCSVM的模拟电路故障诊断方法

王俭臣,单甘霖,段修生,张岐龙   

  1. (军械工程学院光学与电子工程系,石家庄 050003)
  • 收稿日期:2011-07-18 出版日期:2012-02-20 发布日期:2012-02-20
  • 作者简介:王俭臣(1987-),男,硕士研究生,主研方向:模拟电路故障诊断,支持向量机;单甘霖,教授、博士生导师;段修生,副教授;张岐龙,博士研究生
  • 基金资助:
    国家部委基金资助项目

Analog Circuit Fault Diagnosis Method Combining OCSVM

WANG Jian-chen, SHAN Gan-lin, DUAN Xiu-sheng, ZHANG Qi-long   

  1. (Department of Optics and Electronics Engineering, Ordnance Engineering College, Shijiazhuang 050003, China)
  • Received:2011-07-18 Online:2012-02-20 Published:2012-02-20

摘要: 基于支持向量机的传统模拟电路故障诊断方法对新故障无检测能力,且可扩展性较差。针对该问题,提出结合一类支持向量机(OCSVM)和多类支持向量机(MCSVM)的故障诊断方法。该方法采用OCSVM对故障数据进行检测和初步分类,采用MCSVM提高分类性能,以弥补OCSVM分类能力的不足。对OCSVM算法进行改进,以提高其检测和分类性能。通过模拟电路故障诊断实验验证OCSVM改进算法和联合故障诊断方法的有效性。

关键词: 模拟电路故障诊断, 支持向量机, 一类支持向量机, 决策函数, 正负类间隔, 参数选择

Abstract: The traditional diagnosis method based on support vector machine lacks new type fault detection ability and expansibility. To solve this problem, a novel diagnosis method based on the combination of One-class SVM(OCSVM) and Multi-class SVM(MCSVM) is proposed. In this method, the OCSVM module is applied to the detection and preliminary classification of fault data, and the MCSVM module is used to improve the classification capability, which is a shortcoming of the OCSVM. Besides, the OCSVM algorithm is improved to perfect its detection and classification ability. Through an analog circuit fault diagnosis experiment, result shows the effectiveness of the improved OCSVM algorithm and the combined fault diagnosis method.

Key words: analog circuit fault diagnosis, Support Vector Machine(SVM), One-class SVM(OCSVM), decision function, distance between positive and negative class, parameter selection

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