Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden.
Sensors (Basel). 2022 Aug 24;22(17):6357. doi: 10.3390/s22176357.
Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.
铁路道岔和交叉（S&C）是铁路网络中关键的高价值资产。此类资产的单个故障都可能导致严重的网络干扰和相当大的经济损失。轨道凹陷是 S&C 的常见轨面缺陷，需要在早期进行检测和估计，以最小化维护成本并提高 S&C 的可靠性。出于实际考虑，由于电力和信号电缆或传感器损坏的风险，沿 S&C 安装有线或无线传感器可能不可靠。为了解决这些问题，本研究提出了一种从安装在转辙机上的加速度计采集和处理振动数据的方法，以提取与 S&C 轨道凹陷缺陷相关的特征。使用孤立森林算法的无监督异常检测方法从特征中生成异常分数。对重要特征进行排名和选择。本文描述了参数调整的过程，并给出了所得到的异常分数。结果表明，所提出的方法是有效的，并且生成的异常分数表明 S&C 轨道凹陷缺陷的健康状况。
