The Signal Processing Of Axle-counting System Based On Fiber Gratings

Axle-counting Devices are used to check the real-time status of railways sections, which are occupied or not. The functions are equal to that of track circuits. The operational principles are based on the computation result using the axle-counting information in the section. At present, track circuits are wildly used in our country. But they are easily influenced by circumstance such as low roadbed bleeder resistance, bad shunting, etc. These instances make wrong operations occur and lead to the malfunction of signaling interlock. These circumstances baffle the improvement of safety and train speed. Axle-counting devices based on electromagnetic induction are poor under the conditions of interference.The fiber gratings are of good sensing characters, such as high accuracy, high reliability, immune of electromagnetic interference, and corrosion resistance. This has led to fiber gratings becoming a wide range of applications for the measurement of strain and temperature. In this paper, the requirements and specialties of the railway axle-counting system are studied. Then with the integration of the properties of fiber grating and computer technology, a kind of solution is proposed, in which fiber Bragg grating (FBG) and fiber chirped grating (FCG) are used.At each end of rail switch two grating sensors are fixed between a crosstie. A change of the effective refractive index of grating period caused by the surrounding environment will cause a shift in the reflective FBG wavelength and FCG intensity change. When a grating measures a strain, the demodulator turns the dynamic strain of rail track into a row of pulse spectrum.In order to get the accurate axle numbers of passing trains, the smoothing program and recognition pulse algorithm is provided to processing the data collected. After smoothing, the random noise is eliminated and the curve becomes much better. With recognition pulse algorithm, the pulses caused by the axle are recognized well. After analyzing massive data, some restrictive conditions are provided to tell abnormal pulses from real ones, such as slope, peak value, and peak width value. The experiments prove that this Axle-counting system can work steadily.