广东工业大学学报 ›› 2015, Vol. 32 ›› Issue (1): 11-15.doi: 10.3969/j.issn.1007-7162.2015.01.003

• 可拓论坛 • 上一篇    下一篇

基于可拓学的故障诊断及预防方法研究

张家宾1,张金春2,李日华2,李超亚1   

  1. 海军航空工程学院 1.研究生大队; 2.基础部,山东 烟台 264001
  • 收稿日期:2014-10-21 出版日期:2015-03-05 发布日期:2015-03-05
  • 作者简介:张家宾(1989-),男,硕士研究生,主要研究方向为系统分析与集成.

Research on Fault Diagnosis and Prevention Based on Extension

Zhang Jia-bin1, Zhang Jin-chun2, Li Ri-hua2, Li Chao-ya1   

  1. 1. Graduate Administrative Group; 2. Department of Basic, Naval Aeronautical and Astronautical University, Yantai 264001, China
  • Received:2014-10-21 Online:2015-03-05 Published:2015-03-05

摘要: 将可拓数据挖掘方法和模式识别技术相结合,用于解决基于大量数据的故障模式识别问题.利用粗糙集理论进行属性约减和权值确定,保证了获得权值的客观性和稳定性.然后用汽车发动机故障诊断的实例验证了模型的可靠性.最后根据基于变换的可拓集分类思想,探讨了故障预防的一种方法.

关键词: 可拓学; 数据挖掘; 故障诊断; 粗糙集

Abstract: The extension data mining methods and pattern recognition technology are combined to solve the problem of fault diagnosis involved with large amounts of data. By taking advantage of the theory of rough set to make attribute reduction and weight value determination, it ensures the objectivity and stability of weights. Then it verifies the reliability of the model with the example of fault diagnosis of automobile engine. Finally, according to the classification method of extension set based on the transform, this paper presents a method for fault prevention.

Key words: Extenics; data mining; fault diagnosis; rough set

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