Online Outlier Detection of FBG Temperature Sensors based on Image Morphology

被引：0
作者：
Jiang, Xuemei [1 ,2 ]
Jiang, Jing [1 ]
Zhang, Xiaomei [1 ,2 ]
Yan, Junwei [1 ]
Hu, Jiwei [1 ,2 ]
Lou, Ping [1 ,2 ]
Xiao, Angran [3 ]
机构：
[1] WUT, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China
[2] WUT, Minist Educ, Key Lab Fibre Opt Sensing Technol & Informat Proc, Wuhan 430070, Hubei, Peoples R China
[3] CUNY, New York City Coll Technol, Dept Mech Engn Technol, Brooklyn, NY 11291 USA
来源：
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) | 2018年
基金：
对外科技合作项目（国际科技项目）;
关键词：
sliding window; online outlier detection; image morphology;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In online temperature monitoring system for CNC machine tools, the temperature detecting data of CNC machine tools sensed by Fiber Bragg Grating (FBG) temperature sensors directly affect the reconstruction of the temperature field. Analysis on the temperature detecting data can provide important information regarding the thermal error of the CNC machine tools indeed. In this paper, a method of the outlier detection is presented. The method uses the image morphology to detect the outlier online. Firstly, the sliding window is adopted to guarantee online performance. Then outliers are detected by applying opening and closing operations and sequential filter based on image morphology. Finally, the proposed method is applied to handle actual data collected by FBG temperature sensors on CNC machine tools. The results show that the method is valid.
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页数：5
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