Outlier Mining Based Abnormal Machine Detection in Intelligent Maintenance

被引：0

作者：

张蕾 ^{[1
]}

曹其新 ^{[1
]}

李杰 ^{[2
]}

机构：

[1] Robotics Laboratory,Shanghai Jiaotong University

[2] NSF I/UCR Center for Intelligent Maintenance Systems,University of Cincinnati

来源：

Journal of Shanghai Jiaotong University(Science) | 2009年 / 14卷 / 06期

基金：

中国国家自然科学基金;

关键词：

intelligent maintenance; outlier mining; swarm intelligence clustering; abnormal machine detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Assessing machine’s performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection algorithm is proposed for this purpose.Firstly,the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor(CBGOF) is presented.Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed.The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines.Finally,a comparison of mobile soccer robots’ performance proves the algorithm is feasible and effective.

引用

页码：695 / 700

页数：6

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