Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Stefano-Franscini Platz 5, 8093 Zürich, Switzerland.
Metrology Department, Swiss Federal Railways (SBB), 3018 Bern, Switzerland.
Sensors (Basel). 2023 Feb 28;23(5):2672. doi: 10.3390/s23052672.
Monitoring information can facilitate the condition assessment of railway infrastructure, via delivery of data that is informative on condition. A primary instance of such data is found in Axle Box Accelerations (ABAs), which track the dynamic vehicle/track interaction. Such sensors have been installed on specialized monitoring trains, as well as on in-service On-Board Monitoring (OBM) vehicles across Europe, enabling a continuous assessment of railway track condition. However, ABA measurements come with uncertainties that stem from noise corrupt data and the non-linear rail-wheel contact dynamics, as well as variations in environmental and operational conditions. These uncertainties pose a challenge for the condition assessment of rail welds through existing assessment tools. In this work, we use expert feedback as a complementary information source, which allows the narrowing down of these uncertainties, and, ultimately, refines assessment. Over the past year, with the support of the Swiss Federal Railways (SBB), we have assembled a database of expert evaluations on the condition of rail weld samples that have been diagnosed as critical via ABA monitoring. In this work, we fuse features derived from the ABA data with expert feedback, in order to refine defection of faulty (defect) welds. Three models are employed to this end; Binary Classification and Random Forest (RF) models, as well as a Bayesian Logistic Regression (BLR) scheme. The RF and BLR models proved superior to the Binary Classification model, while the BLR model further delivered a probability of prediction, quantifying the confidence we might attribute to the assigned labels. We explain that the classification task necessarily suffers high uncertainty, which is a result of faulty ground truth labels, and explain the value of continuously tracking the weld condition.
监测信息可以通过提供有关状态的信息来促进铁路基础设施的状态评估。此类数据的主要实例是轴箱加速度（ABA），它可以跟踪车辆/轨道的动态相互作用。此类传感器已安装在专用监测列车上，以及欧洲的现役车载监测（OBM）车辆上，从而可以对铁路轨道状态进行连续评估。但是，ABA 测量值存在不确定性，这些不确定性源于噪声污染数据和非线性轨道/车轮接触动力学以及环境和操作条件的变化。这些不确定性给通过现有评估工具对焊缝的状态评估带来了挑战。在这项工作中，我们使用专家反馈作为补充信息源，这使得可以缩小这些不确定性的范围，并最终改进评估。在过去的一年中，在瑞士联邦铁路（SBB）的支持下，我们已经收集了有关通过 ABA 监测被诊断为关键的焊缝样本状态的专家评估数据库。在这项工作中，我们融合了从 ABA 数据中提取的特征以及专家反馈，以改进对有缺陷（缺陷）焊缝的检测。为此，我们使用了三种模型；二分类和随机森林（RF）模型，以及贝叶斯逻辑回归（BLR）方案。RF 和 BLR 模型被证明优于二分类模型，而 BLR 模型进一步提供了预测概率，量化了我们可能归因于分配标签的置信度。我们解释说，分类任务必然存在很高的不确定性，这是由于有缺陷的真实标签所致，并解释了持续跟踪焊缝状况的价值。
