Bardakov Vladimir V, Marchenkov Artem Yu, Poroykov Anton Yu, Machikhin Alexander S, Sharikova Milana O, Meleshko Natalya V
Moscow Power Engineering Institute, 14 Krasnokazarmennaya, 111250 Moscow, Russia.
Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences, 15 Butlerova, 117342 Moscow, Russia.
Sensors (Basel). 2021 Sep 27;21(19):6457. doi: 10.3390/s21196457.
We address non-contact detection of defects in the railway rails under their dynamic loading and propose to combine digital image correlation (DIC) and finite element modeling (FEM). We show that accurate model of defect-free rail operating at the same loading conditions as the inspected one provides a reliable reference for experimental data. In this study, we tested the rail samples with artificial and fatigue defects under cyclic loading, calculated displacement and stress distributions at different locations of the cracks via DIC and validated the obtained results by FEM. The proposed DIC-FEM approach demonstrates high sensitivity to fatigue cracks and can be effectively used for remote control of rails as well as for non-destructive testing of various other objects operating under dynamic loads.
我们研究了在动态载荷下铁路轨道缺陷的非接触检测，并建议将数字图像相关（DIC）和有限元建模（FEM）相结合。我们表明，在与被检测轨道相同的加载条件下运行的无缺陷轨道的精确模型为实验数据提供了可靠的参考。在本研究中，我们对在循环载荷下带有人工缺陷和疲劳缺陷的轨道样本进行了测试，通过数字图像相关计算了裂纹不同位置处的位移和应力分布，并通过有限元建模验证了所得结果。所提出的数字图像相关 - 有限元建模方法对疲劳裂纹具有高灵敏度，可有效用于轨道的远程监测以及对在动态载荷下运行的各种其他物体的无损检测。
