Online outlier detection of FBG temperature sensors based on image morphology

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.