计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (22): 31-34.DOI: 10.3778/j.issn.1002-8331.2008.22.009

• 博士论坛 • 上一篇    下一篇

具有学习能力的智能诊断系统研究

陈蔼祥1,2,伍丽华2,姜云飞2   

  1. 1.广东商学院 数学与计算科学学院,广州 510320
    2.中山大学 软件研究所,广州 510275
  • 收稿日期:2008-06-23 修回日期:2008-07-10 出版日期:2008-07-11 发布日期:2008-07-11
  • 通讯作者: 陈蔼祥

Research on intelligent diagnosis system with machine learning

CHEN Ai-xiang1,2,WU Li-hua2,JIANG Yun-fei2   

  1. 1.School of Mathematics and Computational Science,Guangdong University of Business Studies,Guangzhou 510320,China
    2.Institute of Software,Zhongshan University,Guangzhou 510275,China
  • Received:2008-06-23 Revised:2008-07-10 Online:2008-07-11 Published:2008-07-11
  • Contact: CHEN Ai-xiang

摘要: 随着电子设备系统的日益复杂化,依靠单一的推理技术的故障系统已难以满足复杂系统的诊断要求,将多种不同的推理技术结合起来的集成诊断系统,能够充分利用各自的优点,从而提高系统诊断的正确性和效率,是目前智能诊断研究的一个发展趋势。将研究复合系统的智能诊断问题,提出在一个复合系统的诊断过程中,通过机器学习,使基于规则的诊断和基于模型的诊断两种诊断技术相互结合,在诊断的不同阶段发挥出各自的作用,从而建立一个融合了RBD和MBD技术优点的、具有一定学习能力的智能诊断系统。

关键词: RBD, MBD, 复合系统, 机器学习, 决策树

Abstract: With the gradual complication of some electronic system,using a single reasoning technology is more and more difficult to meet the demand of the fault diagnosis.Combining two or more reasoning technique is a trend of development of intelligent diagnosis,which can make use of their advantages and improve its efficiency.In this paper the intelligent diagnosis problem is discussed,a diagnosis architecture for the composite system is proposed,which combining rule-based diagnosis and model-based diagnosis.These two approaches work efficiently in different stage of the fault diagnosis system and with machine learning.

Key words: Rule-Based Diagnosis(RBD), Model Based Diagnosis(MBD), composite system, machine learning, decision trees