Sysyn Mykola, Przybylowicz Michal, Nabochenko Olga, Kou Lei
Institute of Railway Systems and Public Transport, Technical University of Dresden, 01069 Dresden, Germany.
Department of the Rolling Stock and Track, Lviv Branch of Dnipro National University of Railway Transport Named after Academician V. Lazaryan, 79052 Lviv, Ukraine.
Sensors (Basel). 2021 May 22;21(11):3609. doi: 10.3390/s21113609.
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.
有砟轨道上部结构的特点是，由于道砟沉降和轨枕空隙的积累，轨道几何形状会相对快速地恶化。存在轨枕空隙的轨道区域与动态载荷增加、振动强烈以及道床与轨枕接触条件不利所导致的几何不平顺不同。这会导致不均匀沉降加速增长，从而产生维护成本高昂的局部不稳定情况，影响运输的可靠性和可用性。最近利用轨道旁和车载监测方法对轨枕支撑条件进行的识别和评估，有助于规划预防措施，以避免或延缓诸如道砟破碎、白点、路基缺陷等局部不稳定情况的发展。本文介绍了旨在开发轨枕支撑识别方法的理论和实验研究。通过使用三梁动态模型进行数值模拟，考虑上部结构和机车车辆的动态相互作用，确定了空隙区域与等效几何不平顺相比动态行为的显著特征。分析了小波尺度图和散点图中时域的频谱特征。此外，理论研究能够从轨道旁测量和车载测量的角度确定动态相互作用的异同。通过使用高速视频记录和数字图像相关（DIC）方法对轨道动态位移进行多点轨道旁测量，介绍了实验研究方法。该方法用于从不同范围的有空隙区域和相应的无空隙参考区域收集统计信息。应用的机器学习方法能够利用从轨道旁测量中提取的小波散射特征准确识别近期的空隙。对车载测量方法应用的案例研究显示了近期空隙识别的适度结果以及改进的潜在途径。
