Rail defects are one of the dominant causes of train derailments and an essential factor affecting transportation safety. Among the rail defects, transverse defects (TDs), which are cracks located transversely in rail heads, are one of the main causes of derailments. When TDs are left undetected, their size expands, leading to rail breaks. Therefore, the railway transportation community is interested in the detection of such defects at speeds that do not obstruct the routine railroad operation. The goal of this research is to develop a novel LDV-based noncontact damage detection system for TDs. The tasks performed herein to achieve this goal (i.e., the objective of the study) were: (i) extensive literature review and in-situ testing to understand the vibrations resulting from the propagating waves in rail, (ii) numerical modeling of the damage detection system, (iii) rigorous laboratory and in-situ testing to understand the noise in LDV measurements as well as to evaluate the performance of the damage detection system, and (iv) analytical work to develop filters to minimize the noise in the LDV measurements. Accordingly, the configuration of the developed damage detection consists of two LDVs attached vertically in front of a rail car to measure guided waves in the rail head, which are induced by rail-wheel interaction. This system uses the LDV measurements to detect a change in the relative amplitudes of the recorded waves caused by a defect in the frequency range between 30 kHz to 100 kHz. The lower cut-off frequency was selected conservatively since it was shown in the literature that guided waves start to localize in the rail head after approximately 15 kHz. The higher cut-off frequency was selected since (i) the guided waves below 100 kHz can be used for transverse defect detection (as the frequency exceeds 100 kHz, waves are susceptible to surface defects), and (ii) the measurements collected from rail during the passage of operating trains showed that the power of the excitations induced by wheel-rail interactions is dominant up to approximately 100 kHz. The main challenge during the development of the system was speckle noise, which is inevitable due to the inherent nature of the measurements performed by LDVs placed on a moving platform. Consequently, the damage detection framework associated with the system operates as follows: 1) in the pre-processing stage, time-varying mean and impulsive noise in the recorded LDV signals are filtered and then the changes in the LDV signals in the frequency range of interest are quantified and monitored using moving standard deviation, 2) in the post-processing stage, two damage features, which are based on the relative change in the moving standard deviations and transfer functions between two measurement points are combined using multivariate statistical analysis to create a damage index that shows the location of rail segments which are affected by a defect. The goal of impulsive noise filtering and transfer functions in the framework is to minimize the speckle noise. The field tests demonstrated that rail segments consisting of a defect can be identified by the developed system.Civil, Architectural, and Environmental Engineerin
