The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, paired with a real-time statistical analysis algorithm, has been realized. This system requires a specialized filtering approach based on electrical impedance matching due to the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. The system’s operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. The prototype based on this technology was tested in October 2014 at the Transportation Technology Center (TTC) in Pueblo, Colorado, and again in November 2015 after incorporating changes based on lessons learned. Results from the 2015 field test are discussed in this paper.
In the field of non-destructive evaluation of structures, 2D and 3D imaging of internal flaws is a critical task. Defect imaging allows making informed follow-up decisions based on the morphology of the flaw. This paper will present advances in ultrasonic tomography for the 2D and 3D visualization of internal flaws in solids. In particular, improvements to the conventional tomographic imaging algorithms have been made by utilizing a mode-selective image reconstruction scheme that exploits the specific displacement field, respectively, of the longitudinal wave modes and the shear wave modes, both propagating simultaneously in the test volume. The specific mode structure is exploited by an adaptive weight assignment to the ultrasonic tomographic array. Such adaptive weighting forces the imaging array to look at a specific scan direction and better focus the imaging onto the actual flaw (ultrasound reflector). Moreover, the introduction of a global matched coefficient, computed through the matching of measured and expected times of flight for each pixel, is illustrated. The benefits deriving from the application of this coefficient to conventional imaging frameworks are shown. This study shows that the adaptive weighing based on wave structure and the integration of the global matched coefficient improve image contrast and resolution compared to a conventional ultrasonic imaging technique based on a delay-and-sum or minimum variance distortionless method. Results will be shown from experimental tests of simulated flaws in solids.
The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, paired with a real-time statistical analysis algorithm, has been realized. This system requires a specialized filtering approach based on electrical impedance matching due to the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. The system’s operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Results from the first field test of the non-contact air-coupled defect detection prototype conducted at the Transportation Technology Center (TTC) in Pueblo, Colorado, in October 2014 are presented and discussed in this paper. The results indicate that the prototype is able to detect internal cracks with high reliability.
The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research
and Development (R&D) grant, is developing a system for high-speed and non-contact rail integrity evaluation. A
prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, in pair with a
real-time statistical analysis algorithm, is under development. Experimental tests results, carried out at the UCSD Rail
Defect Farm, indicate that the prototype is able to detect internal rail defects with high reliability. Extensions of the
system are planned to add rail surface characterization to the internal rail defect detection.
The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail integrity evaluation. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection in pair with a real-time statistical analysis algorithm has been realized. This solution presents an improvement over the previously considered laser/air-coupled hybrid system because it replaces the costly and hard-to-maintain laser with a much cheaper, faster, and easier-to-maintain air-coupled transmitter. This system requires a specialized filtering approach due to the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a LISA algorithm. Many of the system operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Experimental tests have been carried out at the UCSD Rail Defect Farm. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. A field test will be planned later in the year to further validate these results. Extensions of the system are planned to add rail surface characterization to the internal rail defect detection.
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