Railway Monitoring Based On Fiber Bragg Grating Sensing Technology

ABSTRACT:Fiber Bragg gratting sensor due to its excellent characteristic of corrosion resistance, resistance to electromagnetic interference and easy to reuse, is gradually used in aerospace, electronic machinery, architecture and communication field.The cross sensitivity problem of temperature and strain of fiber Bragg gratting sensor have been researched in this paper. The research include formation principle and common solution in the field. A double layer encapsulation structure of optical fiber Bragg grating sensor have been proposed in this paper, andFEA(finite element analysis) method have been used for simulating the stain relation between wheel and tract. The result of FEA method provides a theoretical basis for dynamic monitoring. This paper expound the process of data analysis and processing. The main content as follows,This paper proposes a double layer encapsulation structure of optical fiber Bragg grating sensor, aimed at reducing the error in stressing measure with big change in environment temperature range.This structure have two parallel steel layer within it embedded two optical fiber Bragg grattting sensors, the bottom layer is used for stress measurement and top layer for temperature compensation. Through SOLIDWORS software the3d model of this structure have been drawn, and through the finite element analysis module we verified the characteristics of the stress is not sensitive to the top layer, so we verified the top grating sensor’s temperature compensation function. Use the encapsulating structure for the actual stress test, the result shows the temperature compensation error is0.1℃.This paper proposed a method which obtained the wheel signal by using sensor group to detect the strain change of track, and then the number of axle of high-speed train can be detected. In order to obtain the accurate stress change of tracks, based on the finite element analysis method, two characteristic position of track have been simulated, the results show that the most appropriate position for sensor installation is close to sleeper and on the bottom of rail track side.Installed3sensors in high-speed train experiment we collect the actual detection data, after data processing by using five trinomial method and FFT filtering method, the Curve became more prominent, more smooth, more conducive to the peak searching. Through the finite element method to calculate the minimum threshold,baseddigital noise has been eliminated.adopt the method of off-line testing extracted from the data on the number of shaft and the actual match, and the number of axle we detected matched the real number.