An approach for wheel flat detection of railway train wheels using envelope spectrum analysis

Mosleh, Araliya; Montenegro, Pedro Aires; Costa, Pedro Alves; Calçada, Rui

doi:10.1080/15732479.2020.1832536

Cited by 45 publications

(35 citation statements)

References 46 publications

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“…In Mosleh et al [ 369 ], the authors presented an approach that allows the identification of wheel defects and wheel flats with the use of a wayside monitoring system. To do so, a methodology of envelope spectrum analysis was applied (artificial intelligence included, according to the database).…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…Moreover, base drift for strain gauges is unavoidable during long-term service, making it impossible to maintain consistency for a long period. For these reasons, and unlike the previous work carried out by Mosleh et al [37], in which only shear responses were considered to detect wheel flats, a parametric study was performed to compare the accuracy of the system using acceleration responses. The wheel flat was identified through the envelope spectrum approach by considering the evaluated shear and the accelerations as inputs.…”

Section: System Description 241 Layout Scheme Of Multisensor Arraysmentioning

confidence: 99%

“…In previous studies, for the successful detection of wheel defects, sensors have been installed along an equivalent wheel perimeter length [21,36]. However, the study performed by Mosleh et al [37] shows that this amount of sensors is not necessary to identify the defective wheel. Installing sensors along an equivalent wheel perimeter length is useful for identifying specific moments when wheel flat impacts occur.…”

Section: Introductionmentioning

confidence: 99%

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Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

et al. 2021

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The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

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“…Wayside monitoring systems are commonly used to detect faulty wheelsets in service. Mosleh et al [7] investigated an envelope spectral analysis approach to detect wheel flats with wayside sensors using a range of 3D simulations based on a train-track interaction model. In contrast to wayside systems, on-board monitoring systems have traditionally been focused on the detection of track defects [8][9][10][11][12] but are more and more considered for vehicle monitoring [13].…”

Section: Introductionmentioning

confidence: 99%

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

et al. 2021

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Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

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An approach for wheel flat detection of railway train wheels using envelope spectrum analysis

Cited by 45 publications

References 46 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

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