System fault detection based on data-driven and complex networks theory

CHEN Yu; HAN Yongming; WANG Zun; GENG Zhiqiang

doi:10.3969/j.issn.0438-1157.2014.11.041

您当前的位置：

首页 >

文章列表页 >

System fault detection based on data-driven and complex networks theory

更新时间：2025-12-10

- System fault detection based on data-driven and complex networks theory
- CIESC Journal Issue 11, Pages: 4503-4508(2014)
- 作者机构：
  
  北京化工大学信息科学与技术学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2014.11.041
  CLC： P39
- Online First：05 November 2014，
  
  Published：05 November 2014
- 稿件说明：
移动端阅览
CHEN Yu, HAN Yongming, WANG Zun, et al. System fault detection based on data-driven and complex networks theory[J]. CIESC Journal, 2014, (11): 4503-4508.
DOI：

CHEN Yu, HAN Yongming, WANG Zun, et al. System fault detection based on data-driven and complex networks theory[J]. CIESC Journal, 2014, (11): 4503-4508. DOI： 10.3969/j.issn.0438-1157.2014.11.041.

摘要

化工过程系统结构的大型化和复杂性

通过单独的机理模型进行故障检测已越来越困难.提出一种基于数据复杂网络理论的过程故障检测方法

利用偏相关系数确定复杂变量间的邻接矩阵

生成过程系统数据变量之间的网络模型

从网络拓扑结构出发

计算系统复杂网络的特征参数

通过对故障模型与非故障模型之间网络特征参数的差异判断系统是否发生故障

进而找到故障点.以TE过程为应用对象

验证了该方法的有效性.

Abstract

Since system structures are more and more complex

it's hard to detect the faults only through physical and chemical mechanism model. In order to overcome these difficulties

a new method based on data-driven and complex network theory is proposed using partial correlation coefficient to determine adjacency matrix of variables

then define the network model. Based on the network topology structure

the characteristics of complex system network are calculated. According to the changing of parameters of network

it can be determined whether a system has failure by contrasting the diffidence between the fault model and normal model. At last

the proposed method is applied to TE process to test its validity.

关键词

Keywords

references

Views

351

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A chemical process quality-related fault detection method based on twin-space parallel regression

Fault detection using grouped support vector data description based on maximum information coefficient

Research on quality-related fault detection method based on VAE-OCCA

Fault diagnosis of proton exchange membrane fuel cell based on nonlinear dynamic model

Fault detection method based on kernel entropy independent component analysis

Related Author

SONG Bing

GUO Tao

SHI Hongbo

TAN Shuai

TAO Yang

MA Yuyang

ZHANG Yihao

WANG Zhenlei

Related Institution

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education

China Railway 14th Bureau Group Corporation Limited

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology

Faculty of Environment and Life, Beijing University of Technology

⁰