Paper Info
Title
Online Outlier Detection Of Fbg Temperature Sensors Based On Image Morphology
Abstract
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.
Year
Venue
Keywords
2018
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)
sliding window, online outlier detection, image morphology
Field
DocType
ISSN
Computer vision,Anomaly detection,Fiber Bragg grating,Sliding window protocol,Monitoring system,Numerical control,Computer science,Outlier,Control engineering,Artificial intelligence,Temperature measurement,Machine tool
Conference
1810-7869
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Xue-Mei Jiang1254.03
Jing Jiang200.68
Xiaomei Zhang3179.13
Junwei Yan431.08
Jiwei Hu5265.82
Ping Lou655.54
Angran Xiao701.01