Outlier Mining Based Abnormal Machine Detection in Intelligent Maintenance  

作　　者：张蕾 曹其新 李杰 

机构地区：[1]Robotics Laboratory,Shanghai Jiaotong University [2]NSF I/UCR Center for Intelligent Maintenance Systems,University of Cincinnati

出　　处：《Journal of Shanghai Jiaotong university(Science)》2009年第6期695-700,共6页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China (No. 50705054)

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.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.

关 键 词：intelligent maintenance outlier mining swarm intelligence clustering abnormal machine detection 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]