Diptojit Datta

An ultrasonic sonar-based ranging technique is introduced for measuring full-field railroad crosstie (sleeper) deflections. Tie deflection measurements have numerous applications, such as detecting degrading ballast support conditions and evaluating sleeper or track stiffness. The proposed technique utilizes an array of air-coupled ultrasonic transducers oriented parallel to the tie, capable of “in-motion” contactless inspections. The transducers are used in pulse-echo mode, and the distance between the transducer and the tie surface is computed by tracking the time-of-flight of the reflected waveforms from the tie surface. An adaptive, reference-based cross-correlation operation is used to compute the relative tie deflections. Multiple measurements along the width of the tie allow the measurement of twisting deformations and longitudinal deflections (3D deflections). Computer vision-based image classification techniques are also utilized for demarcating tie boundaries and tracking the spatial location of measurements along the direction of train movement. Results from field tests, conducted at walking speed at a BNSF train yard in San Diego, CA, with a loaded train car are presented. The tie deflection accuracy and repeatability analyses indicate the potential of the technique to extract full-field tie deflections in a non-contact manner. Further developments are needed to enable measurements at higher speeds.

It is known in the railroad maintenance engineering community that the deflection of railroad ties is an indicator of the quality of the tie–ballast interface, whose deterioration may cause dangerous train derailments. A new technology is proposed to reconstruct the full-field deflection profile of railroad ties in-motion by means of non-contact ultrasonic testing and computer vision techniques. The sensing layout consists of an array of air-coupled capacitive transducers (operated in pulse-echo sonar-based ranging mode) and a high frame-rate camera, rigidly connected to the main frame of a moving train car. The acquisition system is programmed such that the synchronized waveforms and images are collected and saved as train car moves. In the processing stage, a supervised machine learning-based image classification approach is developed to demarcate the tie boundaries. For this purpose, the Speeded-Up Robust Features (SURF) and Bag of Visual Words (BOVW) algorithms are employed to encode images into condensed feature vectors, which are subsequently fed into the Support Vector Machine (SVM) to train a classifier. The relative deflections of the identified ties are eventually computed by tracking the time-of-flight of the reflected waves from the surfaces flagged as tie. An image processing technique is also developed to estimate the spatial resolution of the tracking system, required to reconstruct the full-field deflection profile of the scanned ties. The importance of such a technique is stressed if the test run is performed without any dedicated positioning system. The proposed ‘tie sonar’ system was prototyped and used to reconstruct the deflection profile of the ties scanned during a series of test runs conducted at slow (walking) speed at the Rail Defect Testing Facility (RDTF) of UC San Diego as well as a BNSF yard in San Diego, CA, with a realistic train load. Further developments of this system should include a performance evaluation at higher speeds (e.g., revenue speed).

This paper presents a high-speed noncontact rail inspection technique that has the potential of detecting internal rail defects at regular (revenue) train speeds. The technique utilizes an array of capacitive air-coupled ultrasonic transducers in continuous recording mode to extract a reconstructed transfer function for a rail segment in a passive manner. The passive approach utilizes the ambient excitation of the rail induced by the wheels of the test car and eliminates the need for a controlled source. A normalized cross-correlation operator with modified Welch's periodogram technique is used to extract the transfer function in a manner that is independent of the uncontrolled excitation source (rolling wheels). Discontinuities in the rail (e.g., joints, welds, and defects) alter the reconstructed transfer function which is statistically tracked using an outlier analysis for detection robustness and sensitivity. Field tests were carried out with a prototype at the Transportation Technology Center Inc. (TTCI) in Pueblo, CO at testing speeds of up to 80 mph. The performance of the system in detecting rail discontinuities was assessed via receiver operating characteristic curves for a range of varying operational parameters such as excitation strength, baseline distribution length, testing speed, and multiple runs.