Modeling wave propagation in damped waveguides of arbitrary cross-section

A wavelet-support vector machine conjunction model for monthly streamflow forecasting

The aim of this work is to demonstrate the use of a commercial finite element package, ABAQUS EXPLICIT, to model ultrasonic guided waves in structural components. The particular application of interest is modeling the interaction of a broadband vertical bending mode with transverse-type defects in railroad tracks. This topic is part of a broader project on high-speed defect detection in rails by long-range ultrasonic inspections. Reflection coefficient spectra in the 20–45 kHz range are obtained for four different sizes and three different orientations of transverse head flaws. A preliminary study of Lamb waves in a free plate helps drawing modeling guidelines for the rail.

Modeling guided wave propagation with application to the long-range defect detection in railroad tracks

This study investigates the accuracy of support vector machines (SVM), which are regression procedures, in modelling reference evapotranspiration (ET0). The daily meteorological data, solar radiation, air temperature, relative humidity and wind speed from three stations, Windsor, Oakville and Santa Rosa, in central California, USA, are used as inputs to the support vector machines to reproduce ET0 obtained using the FAO-56 Penman-Monteith equation. A comparison is made between the estimates provided by the SVM and those of the following empirical models: the California Irrigation Management System (CIMIS) Penman, Hargreaves, Ritchie and Turc methods. The SVM results were also compared with an artificial neural networks method. Root mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. The comparison results reveal that the support vector machines could be employed successfully in modelling...

https://www.tandfonline.com/doi/pdf/10.1623/hysj.54.5.918

Evapotranspiration modelling using support vector machines

Wavelet-based outlier analysis for guided wave structural monitoring: Application to multi-wire strands

Measuring high-frequency wave propagation in railroad tracks by joint time-frequency analysis

This paper presents the results from a pilot study of a 'smart system' used for defect detection in railroad rails, particularly the critical transverse-type defects. The experimental data used to train the pattern recognition 'smart system' were extracted from experiments conducted during a previous long-range ultrasonic guided wave study conducted at the University of California, San Diego. Reflection coefficient plots corresponding to a variety of transverse and oblique defects were shown to provide features that were successfully used to train a 'smart system' to identify the defects automatically. This paper presents a brief introduction to support vector machines, followed by a description of the procedure used to determine the best data to be used to train the 'smart system', and concludes with lessons learned during this study.

Automatic defect classification in long-range ultrasonic rail inspection using a support vector machine-based ?smart system?

Noncontact methods to generate and detect ultrasonic waves for the testing of railroad tracks are being investigated by a number of researchers. While noncontact testing offers potential advantages over conventional contact testing, it generally suffers from a poor signal to noise ratio of the measurements. One powerful tool to filter out noise from ultrasonic signals is the discrete wavelet transform, which can be used in high speed applications due to its computational efficiency. In this paper, discrete wavelet transform filter bank processing is applied to two noncontact ultrasonic systems for rail testing, namely a hybrid laser/air coupled setup for the detection of transverse cracks and a purely air coupled setup for the detection of longitudinal cracks. The discrete wavelet transform proves effective in filtering noise from the signals by eliminating the need for averaging in the hybrid setup and reducing to three the number of averages needed in the air coupled setup. Data compression is an added product of the discrete wavelet transform processing. The study shows the importance of selecting the proper mother wavelet function for best processing performance. In particular, the shape of the wavelet should be similar to that of the signal being analyzed. The discrete wavelet transform emerges as a powerful tool to ease the transition to the field of noncontact ultrasonic rail testing.

Improvements in noncontact ultrasonic testing of rails by the discrete wavelet transform

Two methods for the detection of structural defects in railroad tracks are discussed. The first method employs air-coupled ultrasonic testing for non-contact probing and ease of transducer positioning. It is shown that resonant standing waves can be successfully generated in the rail cross-section to alleviate the challenges associated with the large acoustic impedance mismatch between air and steel. The cross-sectional inspection is particularly well-suited for the detection of longitudinal defects. The second method uses structural vibrations of the track for long-range detection of transverse and oblique defects. In the presence of various defect types and sizes, the reflection coefficient spectra of broadband longitudinal, vertical and lateral vibrations are extracted by a joint time-frequency analysis based on the wavelet transform. These results can be useful for the development of a rail defect detection system based on automatic pattern recognition.

Ultrasonic NDE of railroad tracks: air-coupled cross-sectional inspection and long-range inspection

The behavior of high-frequency transient vibrations propagating in railroad tracks is relevant to the field of long-range structural inspection of rails. While a large amount of theoretical and numerical work has been done to predict rail vibrations, experimental data have been seldom obtained due to the complex multimode and dispersive behavior of the waves. In this work a joint time-frequency analysis based on the Gabor wavelet transform is employed for measuring longitudinal and lateral vibrations propagating in a rail section in the 1000 Hz–7000 Hz range. The wavelet transform is capable of extracting both the group velocity dispersion curves and the frequency-dependent attenuation of these vibrational modes. The wavelet transform optimizes the time-frequency resolution of the dynamic signals and simply requires a single measurement point. These features make the technique well-suited for the experimental study of rail vibrations.

John Mcnamara

Papers

Measuring high-frequency wave propagation in railroad tracks by joint time-frequency analysis

Automatic defect classification in long-range ultrasonic rail inspection using a support vector machine-based ?smart system?

Improvements in noncontact ultrasonic testing of rails by the discrete wavelet transform

Ultrasonic NDE of railroad tracks: air-coupled cross-sectional inspection and long-range inspection

Wavelet transform for characterizing longitudinal and lateral transient vibrations of railroad tracks