[1]彭霜霜,王洪春.基于因果图最小割集和最小径集在故障系统中的诊断[J].南京师范大学学报(工程技术版),2015,15(03):060.
 Peng Shuangshuang,Wang Hongchun.A Diagnosis Approach Based on Minimal Cut Sets andMinimal Path Sets of Causality Diagram[J].Journal of Nanjing Normal University(Engineering and Technology),2015,15(03):060.
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基于因果图最小割集和最小径集在故障系统中的诊断
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
15卷
期数:
2015年03期
页码:
060
栏目:
计算机工程
出版日期:
2015-09-20

文章信息/Info

Title:
A Diagnosis Approach Based on Minimal Cut Sets andMinimal Path Sets of Causality Diagram
作者:
彭霜霜王洪春
重庆师范大学数学学院,重庆 401331
Author(s):
Peng ShuangshuangWang Hongchun
College of Mathematics,Chongqing Normal University,Chongqing 401331,China
关键词:
因果图故障诊断最小割集最小径集
Keywords:
causality diagramfault diagnosisminimal cut setsminimal path sets
分类号:
TP39
文献标志码:
A
摘要:
因果图主要用于故障诊断和故障分析,运用因果图模型的原理和方法,对基于因果图最小割集和最小径集的定量故障诊断方法进行研究,并给出飞行器发动机滑动压力指示警告系统的诊断实例,为系统故障源的查找提出简洁有效的方法. 首先将因果图转换成因果树,再定义最小割集和最小径集,最后结合实例给出诊断方案步骤.
Abstract:
Causality diagram is mainly used for fault diagnosis and fault analysis. The paper,by using the principles and methods of causal graph models conducts a quantitative research on minimal cut sets and minimal path sets fault diagnosis method based on causal diagram,and gives instruction in aircraft engines sliding pressure warning system diagnosis example,and proposes a simple and effective mothod to find the source failure of the proposed system is. Firstly,the causality diagram is converted into a causality tree,then the minimal cut sets and minimal path sets are defined,and finally the steps of the diagnostic programs are given by combining examples.

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相似文献/References:

[1]文利燕,彭晨,裴灵犀,等.基于模型的网络控制系统故障诊断综述[J].南京师范大学学报(工程技术版),2011,11(01):039.
 Wen Liyan,Peng Chen,Pei Lingxi.Overview on Fault Diagnosis of Networked Control System Based on the Models[J].Journal of Nanjing Normal University(Engineering and Technology),2011,11(03):039.
[2]胡寿松,徐德友,张敏.基于粗糙神经网络的歼击机操纵面智能故障诊断[J].南京师范大学学报(工程技术版),2004,04(03):001.
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备注/Memo

备注/Memo:
收稿日期:2014-10-16. 
基金项目:国家社科基金(13BTJ008)、重庆高校创新团队建设计划资助项目(KJTD201308). 
通讯联系人:王洪春,博士,教授,研究方向:人工智能,因果图推理等. E-mail:wang-hongchun@tom.com
更新日期/Last Update: 2015-09-20