Fault Detection of Associated Complex Systems Using Integrated Complex Network Theory with SVM

被引：0

作者：

Zhao, Huiyang ^{[1
,2
]}

Hu, Yanzhu ^{[1
]}

Ai, Xinbo ^{[1
]}

Hu, Yu ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Automat Sch, Beijing, Peoples R China

[2] Xuchang Univ, Sch Informat Engn, Xuchang, Peoples R China

来源：

PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2017年

基金：

中国国家自然科学基金;

关键词：

fault detection; complex network; SVM; industrial system; DIAGNOSIS; CAUSALITY; BEARINGS; VECTOR;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Fault detection in industrial process is a popular research because it is very important for production and quality. In this paper, we proposed a novel method, integrated complex network theory with support vector machine(SVM), for fault detection of associated complex systems. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The experiments show that this method works well and can be a useful supplement for fault detection of associated complex systems.

引用

页码：2659 / 2664

页数：6

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