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The cross-sectional geometry of a rail is complex, and numerous guided wave modes can be propagated in rails. In order to select a mode which is the most suitable for detecting a specific crack on a rail, a mathematical model of guided wave mode selection is constructed. The model is composed of a modal vibration factor and a modal orthogonal factor. By setting a reasonable vibration coefficient and orthogonal coefficient, the mode with the highest sensitivity to cracks is selected for crack detection. Taking a vertical crack on the rail bottom as an example, mode 1 at a frequency of 60 kHz is selected as the most suitable detection mode. At the same time, mode 7 and mode 11 are selected as comparative modes, and these three modes are simulated to detect rail cracks. Among them, mode 1 is the best, which verifies the correctness of the mode selection model. In addition, vertical cracks are manufactured artificially on the side of the rail bottom. The cracks are successfully detected by mode 1, and the positioning error is 0.07 m. After correction, the error is reduced to 0.02 m. The model can effectively select guided wave modes suitable for detecting arbitrary cracks on rails, which provides a theoretical solution for rail crack detection.

Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.

Cement asphalt mortar (CA) disengagement of a ballastless track will induce changes of dynamic response of a passing vehicle, which can accordingly be used to estimate the disengagement degree. In this paper, a novel method called CA mortar disengagement degree estimation algorithm (CMDEA) is proposed through an analysis of wheel acceleration of a passing vehicle. The disengagement degree estimation is transformed into an optimization problem by regarding the CA mortar disengagement degree as a parameter of a vehicle-track coupling model. An improved genetic algorithm with a shifting window is employed for the parameter optimization, which is split into a number of phases and whose initial values are given in terms of a priori probabilities. The accuracy and robustness of the estimation are discussed, and the results are compared with regular genetic algorithm. The simulation results show that CMDEA can estimate CA mortar degrees with an acceptable accuracy. Meanwhile, the proposed algorithm has the advantages of a lower error value and much shorter computation time. Moreover, the robustness of the algorithm under different vehicle speeds, track irregularities, and signal noise levels is also verified.