Railway wheel-flat detection and measurement by ultrasound

Brizuela, José; Fritsch, C.; Ibanez, A.

doi:10.1016/j.trc.2011.04.004

Cited by 54 publications

(35 citation statements)

References 11 publications

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“…In (J. Brizuela, Fritsch, & Ibáñez, 2011) the ultrasound technique to railway wheel flat detection and measurement was proposed. The method uses Rayleigh wave ultrasonic pulses sent at regular intervals over measuring rail.…”

Section: Wheel-flat Detection Systemsmentioning

confidence: 99%

Wheel-flat detection on trams using envelope analysis with Hilbert transform

Nowakowski

Komorski

Szymański

et al. 2019

Lat. Am. j. solids struct.

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The wheel-flat defect is one of the many issues decreasing the level of and comfort in light rail vehicles. The two main causes of wheel-flats are temporary or complete wheel blocking. The negative effect is the increase of the dynamic phenomenons in the wheel-rail interaction, which are impulsive in their nature. It could prove to be dangerous for the safety of the ride. Therefore, a regular wheel surface monitoring and wheel-flats detecting would be well-founded to the fast detection of a flat point and to take remedial measures. The main aim of the article is to present the algorithm, according to which the wheel-flat detection during tram passage is possible. Several vibration transducers were mounted on the rail and measured vibration amplitude during trams pass-by. The proposed method is based on the vibration signal processing in the time and frequency domain. The Hilbert transform was used in the algorithm and all research analyses. The carried out experimental study and the analysis of the results of the method, show a high efficiency in the wheel flat detection. KeywordsTram, wheel-flat, Hilbert transform, envelope analysis Wheel-flat detection on trams using envelope analysis with Hilbert transform Tomasz Nowakowski et al.

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Section: Wheel-flat Detection Systemsmentioning

confidence: 99%

Wheel-flat detection on trams using envelope analysis with Hilbert transform

Nowakowski

Komorski

Szymański

et al. 2019

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

show abstract

“…AE and laser technology has also been used in the wayside 3 of 23 measurement [20]. Brizuela, Jose et al [21,22] calculated the roundtrip time-of-flight (RTOF) of wheel flat impact echo to detect the wheel defect. The biggest limitation of this method is that high accuracy is only achieved when the train moves at a relatively low speed (5 to 15 km/h).…”

Section: Introductionmentioning

confidence: 99%

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

et al. 2020

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Featured Application: Wheel flats are one of the most threatening defects during the service lifespan of railway vehicle wheels. This study proposes a new long-term monitoring method for wheel flats based on multisensor arrays. The dynamic strain responses of rails are captured by sensor arrays mounted on the rail web, ensuring that all the wheels are assessed during the train passage. Through data fusion among multiple sensors, the method locates the specific position of wheel defects. This study provides potential guidance for the maintenance of vehicles soon after the occurrences of defects.Abstract: Wheel flats have become a major problem affecting the long-term service of railway systems. Wheels with flats create intermittent impact loads to trains and rails. This not only accelerates the deterioration of vehicle and track components but also leads to abnormal wheel-rail contact conditions. An effective method for detecting wheel conditions is urgently needed to ensure the operation of the railway and provide guidance for the repair of wheels. However, most previous researches have used qualitative detection methods, and hence have been unable to achieve accurate positioning of the wheel flats. In addition, the theoretical basis for the layout scheme for wheel flat detection sensors is lacking, making it impossible to meet the needs of field applications. In this study, we simulated the spatial distribution characteristics of rail strain, under different wheel flat conditions, and based on this, a layout scheme of multisensor arrays was proposed which more effectively captured the responses of the wheel flats. A wheel flat recognition and precise positioning method based on multisensor fusion was designed. The algorithm was validated through the combination of experimental and simulation methods. The result shows that the algorithm can ideally detect and locate the wheel flats under complex conditions.

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“…Visual inspections are supplemented with eddy current [28] and ultrasound measurements [1] for detection of cracks. However, some conditions of IRJs can still not be detected by these three methods.…”

Section: Introductionmentioning

confidence: 99%

Health condition monitoring of insulated joints based on axle box acceleration measurements

Molodova

Oregui

Núñez

et al. 2016

Engineering Structures

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a b s t r a c tThis paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment, the most relevant being a set of frequency bands in the ABA power spectrum. A detection algorithm is proposed based on the derived frequency characteristics of the ABA signal. We compared the responses of IRJs in good condition with those in poor condition. Track inspections were performed to validate the health condition monitoring methodology in different IRJs of the Dutch railway network. The hit rate of IRJs detection was 84% for two validated tracks. The damage assessment procedure allowed to prioritize the IRJs that require maintenance. This information is useful for the railway inframanagers as it allows to predict where safety compromises will be faced.

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Railway wheel-flat detection and measurement by ultrasound

Cited by 54 publications

References 11 publications

Wheel-flat detection on trams using envelope analysis with Hilbert transform

Wheel-flat detection on trams using envelope analysis with Hilbert transform

Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays

Health condition monitoring of insulated joints based on axle box acceleration measurements

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