Analysis of Railway Track Irregularities with Convolutional Autoencoders and Clustering Algorithms

Niebling, Julia; Baasch, Benjamin; Kruspe, Anna

doi:10.1007/978-3-030-58462-7_7

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…Niebling et al [ 371 ], staying in the mainstream of low-cost sensor applications—e.g., Knight-Percival et al [ 372 ]—on in-service trains, suggested deep learning ( artificial intelligence ) in order to analyze large volumes of sensors’ measurement data. The authors combined common signal processing methods and deep convolutional autoencoders and clustering algorithms.…”

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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“…Recent work mainly made use of unsupervised machine learning methods and addressed e.g. the separation of vibration components caused by vehicle and rail using blind signal separation (16) or clustering of anomalies by usage of convolutional autoencoders (17) . In this paper, the set-up allows for supervised learning since the processed ABA data can be compared to reference data.…”

Section: Introductionmentioning

confidence: 99%

Detecting corrugation defects in harbour railway networks using axle-box acceleration data

Heusel

Baasch

Riedler

et al. 2022

insight

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Sea- and inner ports are intermodal traffic nodes that play an important role in transportation, especially in the transportation of goods. The appearance of track defects in a harbour railway network has a negative impact on safety, cost and comfort (for example due to noise emission). The analysis of data obtained by embedded acceleration sensors, which are installed at the axle box of an equipped in-service vehicle, allows for continuous condition monitoring of the track infrastructure. The German Aerospace Center (DLR) develops prototypical modular multi-sensor systems that are used in different operational environments, including on a shunter locomotive operating in an industrial harbour railway network in Braunschweig, Germany. Within the HavenZuG research project, extensive rail longitudinal profile and track geometry measurements have been performed using established inspection methods to obtain the true underlying condition of the railway network. In the present paper, methods for gaining relevant information from the axle-box acceleration (ABA) data are presented and validated with the given reference data. The focus is on detecting defects that are visible in the rail longitudinal profile, mainly rail corrugation. It can be shown that ABA data gathered during everyday shunting operation can be used for detecting corrugation and for inferring rail longitudinal profile parameters.

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“…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]. The advantage of on-board monitoring systems is that the wheel is monitored continuously and not only when the vehicle passes a track side monitoring site.…”

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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Analysis of Railway Track Irregularities with Convolutional Autoencoders and Clustering Algorithms

Cited by 10 publications

References 9 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

Detecting corrugation defects in harbour railway networks using axle-box acceleration data

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

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