Section of Railway Engineering, Delft University of Technology, Delft, The Netherlands.
Delft Center For Systems and Control, Delft University of Technology, The Netherlands.
Risk Anal. 2017 Aug;37(8):1495-1507. doi: 10.1111/risa.12836. Epub 2017 May 31.
Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.
铁路基础设施监测对于确保铁路运输安全至关重要。铁轨故障不仅会对列车延误和维护成本产生重大影响，还会对乘客安全造成影响。本文旨在通过分析一种称为凹坑的铁轨表面缺陷类型，评估铁轨故障的风险。这些凹坑是从摄像头拍摄的大量记录中自动检测到的。我们提出了一种用于自动检测凹坑的图像处理方法，特别是那些容易导致铁轨断裂的严重类型。我们测量凹坑的视觉长度，并将其用于建模故障风险。为了评估铁轨故障风险，我们根据凹坑的增长来估计铁轨故障的概率。此外，我们还进行了严重程度和裂纹扩展分析，以在三种不同的增长情况下考虑铁轨交通荷载对缺陷的影响。针对荷兰铁路网络繁忙轨道上不同裂纹扩展长度的几个凹坑样本，给出了故障风险估计。结果表明了所提出方法的实用性和效率。
