A Data Splicing Method for Measuring Rail Corrugation Under Pitching Vibration

Rail corrugation is the geometric structure of the rail top surface, and it directly related to the safety and comfort of rail transportation. The monitoring of rail corrugation is the key work for rail maintenance department. Currently, the 2D laser displacement sensor is being used to measure the rail corrugation. However, the 2D sensor is easily affected by pitching vibration under complex measurement environment, which leads to large errors in the final measurement results. For solving the problem of pitching vibration in dynamic measuring rail corrugation, a data splicing method based on deviation recognition and calibration is proposed in this study. Firstly, the blind recognition of sensor deviation is applied to identify the measurement state of sensor in each data sampling. Then, a mapping method is proposed to position the actual repeated measurement interval according to geometric relationship between the deviation angle and the offset in the longitudinal direction. Finally, the modified DTW model is proposed for data registration and the data splicing can be achieved according to the registration results. Extensive experiments are carried on both indoor experimental platform and outdoor platform. The experimental results indicate that our proposed method can accurately recognize the deviation of sensor and can calibrate the deviated data set, and its performance on the rail corrugation measurement under the pitching vibration case outperforms previous data splicing work. Thus, it is with the potential engineering application.