Railroad Sleeper Condition Monitoring Using Non-Contact in Motion Ultrasonic Ranging and Machine Learning-Based Image Processing

Abstract

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.

Railroad condition monitoring with distributed acoustic sensing: an investigation of deep learning methods for condition detection

“…Using signal and data processing techniques, accurate deflections of crosstie were measured. 16 Distributed acoustic sensing (DAS) uses optical fiber cables as sensors to detect and analyze sounds and vibrations along their length, and it has gained increasing attention recently. [17][18][19][20] DAS transforms an optical fiber into an array of strain measurement tools.…”

“…applied signal processing techniques including time gating and cross-correlations. Using signal and data processing techniques, accurate deflections of crosstie were measured 16 …”

Seeking a Sufficient Data Volume for Railway Infrastructure Component Detection with Computer Vision Models

“…This is due to the specific characteristics of faults for different components, and therefore, many studies are focusing on only one component of the rail infrastructure. Studies include the detection of wheel defects [17,18], the identification of bolt corrosion [19,20], assessing ballast support for sleepers [21], aiding in the design of prestressed concrete railway sleepers [22], the recognition of rail surface cracks [23], capturing fastener defect detection [24], and monitoring bridges' condition [25,26]. However, fault identification is impossible without accurate object detection (OD) in the previous step (1).…”