A Two-Stage Detection Method of Rigid Pantograph Catenary Contact Points Using DCNNS

Zheng, Danyang; Lu, Xuemin; Quan, Wei; Peng, Yuchen; Liu, Yueping; Chen, Jim X.

doi:10.1088/1742-6596/1754/1/012071

Cited by 3 publications

(2 citation statements)

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“…Another trend in the application of image and video-processing methods is related to deep-learning computer vision algorithms such as YOLO3 and others [ 71 , 72 , 73 , 74 , 75 ]. They provide the object recognition, that in case of the sleeper support diagnostics could automatically bring the necessary information about the loading position, loading type, image quality improvement, etc.…”

Section: In Situ Measurements Of Rail Deflection In Void Zonementioning

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: In Situ Measurements Of Rail Deflection In Void Zonementioning

confidence: 99%

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

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Figure 4 shows the monitoring system based on normal vehicle outweigh other platforms, this reveals a trend that normal-vehicle-based monitoring system is of more interest for that it can provide more frequent inspection than other types. The normalvehicle-based monitoring can be found in [12] [13].…”

Section: Resultsmentioning

confidence: 99%