Vibration-based damage detection of rail fastener clip using convolutional neural network: Experiment and simulation

Yuan, Zhandong; Zhu, Shengyang; Yuan, Xuancheng; Zhai, Wanming

doi:10.1016/j.engfailanal.2020.104906

Cited by 37 publications

(15 citation statements)

References 19 publications

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“…Various data-driven approaches were proposed in numerous studies on railway infrastructure diagnostics over the last years. A one-dimensional convolutional neural network was proposed in the study [ 80 ] for damage detection of fastener clips using time-domain data recorded by accelerometers on the rail. The measurement data were acquired from laboratory impact tests for the track system under different health conditions of fastener clips.…”

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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“…A rail defect detection system consists not only of an instrumented vehicle with a set of sensors but also a data acquisition system, a data analysis tool, and data processing algorithms. Railway track inspection uses non-destructive techniques [ 15 , 16 ] which can be divided into vision-based methods such as laser scanners [ 17 ] and video image processing [ 18 ], as well as displacement-based methods such as acceleration measurement [ 19 ], which measure the vertical movement of vehicles to detect damage to rails and other track components. Thermal imaging cameras are also of great importance in diagnosing rail defects.…”

Section: Literature Analysismentioning

confidence: 99%

“…Machine learning is considered an effective tool for detecting anomalies based on pattern or image analysis. Yuan et al [ 19 ] present an example of rail fastener clip damage detection by artificial neural network realization. In this study the single element is analysed for damage, and the neural network is proved to be a quick and reliable method for that.…”

Section: Information System Supporting Thermographic Inspectionmentioning

confidence: 99%

“…Machine learning is considered an effective tool for detecting anomalies based on pattern or image analysis. Yuan et al [19] present an example of rail fastener clip damage detection by artificial neural network realization. In this study the single element is ana- The data range analysis system consists of the following components: The manual analysis module contains a set of tools processing the thermogram for direct visual inspection by the operator.…”

Section: The Concept Of Machine Learning Applied To Analysis Of Thermograms For Fault and Damage Inspectionmentioning

confidence: 99%

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Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis

Stypułkowski

Gołda

Lewczuk

et al. 2021

Sensors

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The safety and reliability of railway transport requires new solutions for monitoring and quick identification of faults in the railway infrastructure. Electric heating devices (EORs) are the crucial element of turnouts. EORs ensure heating during low temperature periods when ice or snow can lock the turnout device. Thermal imaging is a response to the need for an EOR inspection tool. After processing, a thermogram is a great support for the manual inspection of an EOR, or the thermogram can be the input for a machine learning algorithm. In this article, the authors review the literature in terms of thermographic analysis and its applications for detecting railroad damage, analysing images through machine learning, and improving railway traffic safety. The EOR device, its components, and technical parameters are discussed, as well as inspection and maintenance requirements. On this base, the authors present the concept of using thermographic imaging to detect EOR failures and malfunctions using a practical example, as well as the concept of using machine learning mechanisms to automatically analyse thermograms. The authors show that the proposed method of analysis can be an effective tool for examining EOR status and that it can be included in the official EOR inspection calendar.

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“…It also plays a significant role in fixing the rail and maintaining the gauge distance. 1 However, with the rapid development of rail traffic systems in China, vibrations due to trains have become frequent in recent years. For example, a rail clip is subjected to a combination of bolt preload and wheel–rail cyclic loading, which can cause fatigue fracture, as illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Failure analysis and fatigue life prediction of high-speed rail clips based on DIC technique

Liu

Jiang

et al. 2021

Advances in Mechanical Engineering

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With the development of rail transportation, the fatigue failure of rail clips has become an issue, which affects the operational safety of trains. In this study, reasons for the fatigue failure of rail clips were investigated to improve their service life. A digital image correlation (DIC) technique was conducted to obtain strain fields, vibration modes, and natural frequencies of a rail clip. The strain and displacement of a rail clip under dynamic cyclic loading were also obtained. A fastener system refinement model was developed to analyze the static, dynamic, and modal responses of the clip. The experimental tests and modal simulation results were mutually verified. The fatigue life was analyzed based on the verified FE model. The results revealed that the maximum strain and minimum fatigue life occur at the heel of the clip, in good agreement with the actual fracture position. As the amplitude and frequency of dynamic cyclic load increased, the fatigue life of the clip decreased sharply. Moreover, the normal wheel–rail force accompanied by high-frequency rail corrugations accelerated crack initiation and reduced the fatigue life. The findings of this study provide guidance for improving the service life of rail clips.

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Vibration-based damage detection of rail fastener clip using convolutional neural network: Experiment and simulation

Cited by 37 publications

References 19 publications

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis

Failure analysis and fatigue life prediction of high-speed rail clips based on DIC technique

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