Technology transfer: Transportation

Standard Research Institute (SRI) has operated a NASA-sponsored team for four years. The SRI Team is concentrating on solving problems in the public transportation area and on developing methods for decreasing the time gap between the development and the marketing of new technology and for aiding the movement of knowledge across industrial, disciplinary, and regional boundaries. The SRI TAT has developed a methodology that includes adaptive engineering of the aerospace technology and commercialization when a market is indicated. The SRI Team has handled highway problems on a regional rather than a state basis, because many states in similar climatic or geologic regions have similar problems. Program exposure has been increased to encompass almost all of the fifty states

Contemporary Inspection and Monitoring for High-Speed Rail System

Non-destructive testing (NDT) techniques have been explored and extensively utilised to help maintaining safety operation and improving ride comfort of the rail system. As an ascension of NDT techniques, the structural health monitoring (SHM) brings a new era of real-time condition assessment of rail system without interrupting train service, which is significantly meaningful to high-speed rail (HSR). This chapter first gives a review of NDT techniques of wheels and rails, followed by the recent applications of SHM on HSR enabled by a combination of advanced sensing technologies using optical fibre, piezoelectric and other smart sensors for on-board and online monitoring of the railway system from vehicles to rail infrastructure. An introduction of research frontier and development direction of SHM on HSR is provided subsequently concerning both sensing accuracy and efficiency, through cutting-edge data-driven analytic studies embracing such as wireless sensing and compressive sensing, which answer for the big data’s call brought by the new age of this transport

Ultrasonic guided wave tomography of pipes: A development of new techniques for the nondestructive evaluation of cylindrical geometries and guided wave multi-mode analysis

This dissertation concentrates on the development of two new tomographic techniques that enable wide-area inspection of pipe-like structures. By envisioning a pipe as a plate wrapped around upon itself, the previous Lamb Wave Tomography (LWT) techniques are adapted to cylindrical structures. Helical Ultrasound Tomography (HUT) uses Lamb-like guided wave modes transmitted and received by two circumferential arrays in a single crosshole geometry. Meridional Ultrasound Tomography (MUT) creates the same crosshole geometry with a linear array of transducers along the axis of the cylinder. However, even though these new scanning geometries are similar to plates, additional complexities arise because they are cylindrical structures. First, because it is a single crosshole geometry, the wave vector coverage is poorer than in the full LWT system. Second, since waves can travel in both directions around the circumference of the pipe, modes can also constructively and destructively interfere with each other. These complexities necessitate improved signal processing algorithms to produce accurate and unambiguous tomographic reconstructions. Consequently, this work also describes a new algorithm for improving the extraction of multi-mode arrivals from guided wave signals. Previous work has relied solely on the first arriving mode for the time-of-flight measurements. In order to improve the LWT, HUT and MUT systems reconstructions, improved signal processing methods are needed to extract information about the arrival times of the later arriving modes. Because each mode has different through-thickness displacement values, they are sensitive to different types of flaws, and the information gained from the multi-mode analysis improves understanding of the structural integrity of the inspected material. Both tomographic frequency compounding and mode sorting algorithms are introduced. It is also shown that each of these methods improve the reconstructed images both qualitatively and quantitatively

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model

The paper presents the development of an intelligent image processing algorithm capable of detecting fatigue defects from images of the rail surface. The links between the defect detection algorithm and 3D models for rail crack propagation are investigated, considering the influence of input parameters (materials, vehicle characteristics, loading conditions). The dynamic behaviour at the wheel-rail interface resulting in contact forces responsible for stressing and straining the rail material are imported from vehicle dynamics simulations. The integration of the simulated results from vehicle dynamics, contact and fracture mechanics models offer more reliable estimation of the stress intensity factors (SIF). Also the sensitivity analysis related to materials, vehicle characteristics, and loading conditions will provide further understanding of the factors that influence crack propagation in rails such as shear stresses, hydraulic pressure, fluid entrapment and squeeze film effect. This novel application of image processing for the detection of rail surface rolling contact fatigue (RCF) damage and automatic incorporation in a crack growth model represents an important contribution to the development of modern techniques for non-destructive rail inspection. This will result in improved planning/scheduling of future rail maintenance (e.g. rail grinding, renewal), less disruptions and reduced track maintenance costs in rail industry

Flaw reconstruction in NDE using a limited number of x-ray radiographic projections

One of the major problems in nondestructive evaluation (NDE) is the evaluation of flaw sizes and locations in a limited inspectability environment. In NDE x-ray radiography, this frequently occurs when the geometry of the part under test does not allow x-ray penetration in certain directions. Other times, the inspection setup in the field does not allow for inspection at all angles around the object. This dissertation presents a model based reconstruction technique which requires a small number of x-ray projections from one side of the object under test. The estimation and reconstruction of model parameters rather than the flaw distribution itself requires much less information, thereby reducing the number of required projections. Crack-like flaws are modeled as piecewise linear curves (connected points) and are reconstructed stereographically from at least two projections by matching corresponding endpoints of the linear segments. Volumetric flaws are modeled as ellipsoids and elliptical slices through ellipsoids. The elliptical principal axes lengths, orientation angles and locations are estimated by fitting a forward model to the projection data. The fitting procedure is highly nonlinear and requires stereographic projections to obtain initial estimates of the model parameters. The methods are tested both on simulated and experimental data. Comparisons are made with models from the field of stereology. Finally, analysis of reconstruction errors is presented for both models