Railway wheels flat detector using Doppler effect

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

doi:10.1016/j.phpro.2010.01.104

Cited by 22 publications

(13 citation statements)

References 3 publications

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“…One year earlier, the same authors proposed the railway wheels flat detector system using Doppler effect (Jose Brizuela, Ibañez, Nevado, & Fritsch, 2010). In this case the wheel-rail contact is analysed by frequency and phase shifts.…”

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.

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“…Configuration of a wheel-flat detector. 48 Vibration. Bracciali and Cascini 51 used an acceleration signal to sense the wheel flat and corrugation in wheel tread by comparing the results of energy and cepstrum analysis with the predetermined thresholds.…”

Section: W1 ¼ P1 ð6þmentioning

confidence: 99%

“…Fibre Bragg Grating sensor Field measurement 10,40,44,45,61 Ultrasonic technique Simulation 48 Test rig 47,48 Field measurement 47…”

mentioning

confidence: 99%

Condition monitoring approaches for the detection of railway wheel defects

Alemi

Corman

Lodewijks

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

110

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Condition monitoring systems are commonly exploited to assess the health status of equipment. A fundamental part of any condition monitoring system is data acquisition. Meaningfully estimating the current condition and predicting the future behaviour of the equipment strongly depends on the characteristic of the data measurement stage. Nowadays, condition monitoring has wide applications in the railway industry and various monitoring approaches have been proposed for the inspection of wheel and rail conditions. In-service condition monitoring of wheels provides the real-time data required for maintenance planning, while in-workshop inspection is normally done at fixed intervals carried out periodically. In-service data acquisition can be divided into on-board and wayside measurements. In this paper, on the basis of these classifications, the existing data acquisition techniques for the monitoring of railway wheel condition are reviewed, and the state-of-the-art methods and required research are discussed.

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“…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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Railway wheels flat detector using Doppler effect

Cited by 22 publications

References 3 publications

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

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

Condition monitoring approaches for the detection of railway wheel defects

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

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