Key Equipment Identification model for correcting milepost errors of track geometry data from track inspection cars

Xu, Peng; Sun, Qiang; Liu, Rengkui; Wang, Futian

doi:10.1016/j.trc.2013.06.006

Cited by 30 publications

(31 citation statements)

References 10 publications

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“…[34] DGPS is cited as a method for correcting location information that can be used when the infrastructure is set up for this, and another method cited for more accurate positioning is the use of radio frequency identification (RFID) tags. However, these two innovations are discounted for reasons of cost in the railway line of interest to [34] in favour of using features in the track itself. In the paper, extensive examples of alignment of collected data to the reference map are shown, but the results are still in error by between 1 and 7 m (approximately).…”

Section: Tachometer/odometermentioning

confidence: 99%

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Weston

Roberts

Yeo

et al. 2015

Vehicle System Dynamics

195

130

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This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feeding their geometry measurements into large databases. These data can be retrieved, but little is currently done with the data other than the generation of reports of track geometry that exceeds predefined thresholds. There are examples of simpler systems that measure some track geometry parameters more or less directly and accurately, but forego parameters such as gauge. Additionally, there are experimental systems that use mathematics and models to infer track geometry using data from sensors placed on an in-service vehicle. Finally, there are systems that do not claim to measure track geometry, but monitor some other quantity such as ride quality or bogie acceleration to infer poor track geometry without explicitly measuring it.

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Section: Tachometer/odometermentioning

confidence: 99%

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Weston

Roberts

Yeo

et al. 2015

Vehicle System Dynamics

195

130

View full text Add to dashboard Cite

show abstract

“…The lengths of the buffer segments are dependent on the milepoint shift of the nonaligned inspection in relation to the reference inspection. In this research, it takes on the value of 10 m as inspections are all processed by KEI (Xu et al., ). Following the convention of naming segments associated with the nonaligned inspection, the segment involving the corresponding segment and the two buffer segments is named the reference segment.…”

Section: A New Methods For Aligning Inspectionsmentioning

confidence: 99%

Optimizing the Alignment of Inspection Data from Track Geometry Cars

Sun

Liu

et al. 2014

Computer aided Civil Eng

Self Cite

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“…Through track geometry data, the state of rail infrastructure can be observed at inspection times in an exact and efficient way. By analyzing the geometry condition of the same track segment over and over again in different inspection runs, one can observe track deterioration including warping deformation through track geometry changes [43]. Therefore, it is reasonable to mine the track geometry data and extract the deterioration-sensitive features for the identification of defective slabs, especially for warping deformation detection.…”

Section: Swd Detection Based On Track Geometry Datamentioning

confidence: 99%

Identification of Temperature-Induced Deformation for HSR Slab Track Using Track Geometry Measurement Data

Liu

2019

Sensors

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Slab track is widely used in many newly built high-speed rail (HSR) lines as it offers many advantages over ballasted tracks. However, in actual operation, slab tracks are subjected to operational and environmental factors, and structural damages are frequently reported. One of the most critical problems is temperature-induced slab-warping deformation (SWD) which can jeopardize the safety of train operation. This paper proposes an automatic slab deformation detection method in light of the track geometry measurement data, which are collected by high-speed track geometry car (HSTGC). The characteristic of track vertical irregularity is first analyzed in both time and frequency domain, and the feature of slab-warping phenomenon is observed. To quantify the severity of SWD, a slab-warping index (SWI) is established based on warping-sensitive feature extraction using discrete wavelet transform (DWT). The performance of the proposed algorithm is verified against visual inspection recorded on four sections of China HSR line, which are constructed with the China Railway Track System II (CRTSII) slab track. The results show that among the 24,806 slabs being assessed, over 94% of the slabs with warping deformation can be successfully identified by the proposed detection method. This study is expected to provide guidance for efficiently detecting and locating slab track defects, taking advantage of the massive track inspection data.

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Key Equipment Identification model for correcting milepost errors of track geometry data from track inspection cars

Cited by 30 publications

References 10 publications

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Optimizing the Alignment of Inspection Data from Track Geometry Cars

Identification of Temperature-Induced Deformation for HSR Slab Track Using Track Geometry Measurement Data

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