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Application of Rough Set Theory in Fault Diagnostic Rules Acquisition
作者姓名:殷晨波  周庆敏  李永生
作者单位:[1]College of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing 210009, China [2]College of Information Science and Engineering, Nanjing University of Technology, Nanjing 210009, China
基金项目:Foundation item: Natural Scientific Research Project of the Education Department of Jiangsu Province in China (No. 05KJB520048)
摘    要:Rough set theory is a new mathematical tool to deal with vagneness and uncertainty. But original rough sets theory only generates deterministic rules and deals with data sets in which there is no noise. The variable precision rough set model (VPRSM) is presented to handle uncertain and noisy information. A method based on VPRSM is proposed to apply to fault diagnosis feature extraction and rules acquisition for industrial applications. An example for fault diagnosis of rotary machinery is given to show that the method is very effective.

关 键 词:故障诊断  计算机技术  可变参数  人工智能化
文章编号:1672-5220(2007)02-0276-04
修稿时间:2006-08-20

Application of Rough Set Theory in Fault Diagnostic Rules Acquisition
YIN Chen-bo,ZHOU Qing-min,LI Yong-sheng.Application of Rough Set Theory in Fault Diagnostic Rules Acquisition[J].Journal of Donghua University,2007,24(2):276-279.
Authors:YIN Chen-bo  ZHOU Qing-min  LI Yong-sheng
Institution:1. College of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing 210009, China
2. College of Information Science and Engineering, Nanjing University of Technology, Nanjing 210009, China
Abstract:Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. But original rough sets theory only generates deterministic rules and deals with data sets in which there is no noise. The variable precision rough set model (VPRSM) is presented to handle uncertain and noisy information. A method based on VPRSM is proposed to apply to fault diagnosis feature extraction and rules acquisition for industrial applications. An example for fault diagnosis of rotary machinery is given to show that the method is very effective.
Keywords:variable precision rough set  fault diagnosis  rules acquisition
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