Development of Rail-Condition Assessment Model Using Ultrasonic Technique

Sadeghi, Javad; Rahimizadeh, Yousef; Khajehdezfuly, Amin; Rezaee, Mohammad; Najafabadi, E. Rajaei

doi:10.1061/jtepbs.0000390

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…To simulate rail foot corrosion, a circular defect is made by a grinding wheel in the bottom of rail foot at XD = 100 mm. Its depth is maximized right below the scan path, which is sequentially sampled at 3 mm, 5 mm and 7 mm as the upper limit of mild, medium and severe corrosion regarding [1] and [3]. At each depth level, including the defect-free situation, scanning tests are carried out on the rail specimen with and without the rail clip.…”

Section: Experimental Implementationmentioning

confidence: 99%

“…1, the corrosion usually initiates at the bottom of rail foot edges where causative stray current flows into the contacting rail clip and tie plate [1], [2]. As a result, it is hardly accessible to popular inspection systems based on ultrasonic wheel probe or visual camera [3], [4]. With the increasing railroad miles and train speed, the need of an efficient online method to detect rail foot corrosion becomes more prominent.…”

Section: Introductionmentioning

confidence: 99%

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Laser-Induced Ultrasonic Guided Waves Based Corrosion Diagnosis of Rail Foot

Wang

Sun

et al. 2023

IEEE Trans. Instrum. Meas.

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Rail foot corrosion is an important issue for electrified railway system. It usually initiates at the bottom of rail foot edges covered by a rail clip and is hardly accessible to conventional online inspection systems. To address this problem, this study combines air-coupled ultrasonic transducer (AUT) and laser-induced ultrasonic guided wave to detect rail foot corrosion. The corrosion sensitivity of the guided wave below 500 kHz is examined considering the impact of rail clip. The results have shown that A0 and S0 are the dominant wave modes near the edge of rail foot. The preferred A0 wave is insensitive to allowable rail foot corrosion below 100 kHz. It is neither sensitive above 270 kHz due to wave mode conversion and reduced wave penetration. The optimal frequency is about 200 kHz where the attenuation of A0 wave can reflect the impact of all levels of corrosion even in the presence of a rail clip. Accordingly, an end-to-end deep learning model is employed for corrosion detection based on 200-kHz air-coupled ultrasonic signals. It can automatically extract signal features for corrosion diagnosis as well as differentiate unknown anomalous signals by environmental interference. It is tested with 8122 labeled signal samples collected from field trial and achieves a detection accuracy of 99.5%. All the anomalous signals are also correctly identified.

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Section: Experimental Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laser-Induced Ultrasonic Guided Waves Based Corrosion Diagnosis of Rail Foot

Wang

Sun

et al. 2023

IEEE Trans. Instrum. Meas.

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“…From the point of view of rail transportation, these tests are extremely important for ensuring rail safety due to their complexity. Automatic methods of inspecting rails have become increasingly popular in recent years due to their ability to monitor large sections of rail track quickly and efficiently [11,13]. They also significantly assist workers responsible for maintaining and inspecting rail infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Selected aspects of the diagnostic process in rail transport

Chrzan,

Ciszewski,

Nowakowski

2023

Rail Vehicles/Pojazdy Szynowe

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The article presents selected results of research in the area of reducing the risk of defects in railway infrastructure and traffic control devices. The first part of the article will discuss selected topics used in a defectoscope car for automated ultrasonic rail inspections related to the identification of joints and flaws. A method based on the identification of joints and flaws using a neural network will be presented. The second part of the article will cover research on the automatic collection of diagnostic data from railway traffic control devices. The solutions presented concern a simulator of railway traffic control device malfunctions, from which data is extracted to populate a database of malfunctions and then used in the inference process. The article will present partial results of research on both systems.

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