Noncontact ultrasonic guided wave inspection of rails

Mariani, Stefano; Nguyen, Thompson V.; Phillips, Robert; Kijanka, Piotr; Scalea, Francesco Lanza di; Staszewski, Wiesław J.; Fateh, Mahmood; Carr, Gary

doi:10.1177/1475921713498533

Cited by 54 publications

(43 citation statements)

References 19 publications

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“…This paper discusses the current stage of development and the first field test results of an enhanced rail inspection prototype for the detection of defects in railroad tracks 20 . The prototype uses an air-coupled sensor to generate ultrasonic waves in the rail and an array of air-coupled sensors to detect those waves.…”

Section: Introductionmentioning

confidence: 99%

Non-contact ultrasonic guided wave inspection of rails: field test results and updates

et al. 2015

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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.

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Section: Introductionmentioning

confidence: 99%

Non-contact ultrasonic guided wave inspection of rails: field test results and updates

et al. 2015

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“…Early detection of surface defects is important to mitigate disastrous consequences of rail breaks. There are different methods to diagnose the condition of rail defects, including ultrasonic measurements, eddy current testing, and guided‐wave–based monitoring . In general, these methods are not able to detect defects in an early stage of growth, i.e., not until the defects are severe.…”

Section: Introductionmentioning

confidence: 99%

A Big Data Analysis Approach for Rail Failure Risk Assessment

et al. 2017

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Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.

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“…These methods are of low efficiency and high cost and are unable to achieve on-line inspection while the train is operating. The current inspection methods for rail detects are primarily the image inspection method [6][7][8][9], ultrasonic inspection method [10,11], signal processing method [12][13][14][15][16], and so on. The image inspection method is to detect the defects by analyzing the acquired images of the rail surface.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Impact Reconstruction Method for Rail Defect Inspection

Zhao

Lin

Yao

et al. 2014

Mathematical Problems in Engineering

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The safety of train operating is seriously menaced by the rail defects, so it is of great significance to inspect rail defects dynamically while the train is operating. This paper presents a two-dimensional impact reconstruction method to realize the on-line inspection of rail defects. The proposed method utilizes preprocessing technology to convert time domain vertical vibration signals acquired by wireless sensor network to space signals. The modern time-frequency analysis method is improved to reconstruct the obtained multisensor information. Then, the image fusion processing technology based on spectrum threshold processing and node color labeling is proposed to reduce the noise, and blank the periodic impact signal caused by rail joints and locomotive running gear. This method can convert the aperiodic impact signals caused by rail defects to partial periodic impact signals, and locate the rail defects. An application indicates that the two-dimensional impact reconstruction method could display the impact caused by rail defects obviously, and is an effective on-line rail defects inspection method.

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Noncontact ultrasonic guided wave inspection of rails

Cited by 54 publications

References 19 publications

Non-contact ultrasonic guided wave inspection of rails: field test results and updates

Non-contact ultrasonic guided wave inspection of rails: field test results and updates

A Big Data Analysis Approach for Rail Failure Risk Assessment

Two-Dimensional Impact Reconstruction Method for Rail Defect Inspection

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