Ultrasonic Synthetic Aperture Focus Techniques (SAFTs) using less than the total number of available array elements to transmit (“sparse” transmissions) have been recently used in both medical imaging and industrial NDT imaging to increase test speed and simplify multiplexer hardware. The challenge of sparse arrays is to obtain a reasonable image quality given the reduced transmitter-receiver combinations available to the beamforming process. This paper proposes a “ultrasparse” SAFT method that employs a minimum number of transmitter elements (from one to four elements only) to obtain an entire Full-Matrix Capture (FMC) set of waveforms. Specifically, a “virtual” FMC is obtained from normalized cross-power spectra between each array element pair in an implementation of “passive” ultrasonic sensing. In order to maintain high image quality without sacrificing imaging speed (e.g. applying a minimal initial time delay and keeping a short time recording window), several key steps have to be taken in this “passive” imaging mode, specifically (a) the use of carefully-designed segment-averaged normalized cross-power spectrum for robust passive reconstruction of the ultrasonic Impulse Response Function ( IRF ) between two receivers, (b) the use of both the causal and acausal portions of the passively-reconstructed IRF s, and (c) the compounding of multiple wave modes in the beamforming process. These steps also ensure the elimination of the near-field blind zone hence potentially enabling near-field imaging. The paper first reviews the theory of passive IRF reconstruction between two receivers, comparing time-averaged cross-correlation vs. segment-averaged normalized cross-power spectrum, and then demonstrates the application to ultrasparse SAFT FMC imaging of drilled holes in an aluminum block using a linear transducer array where only one to four elements are used in transmission.

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).