The need for the maintenance of railway track systems have been increasing.
Traditional methods that are currently being used are either inaccurate, labor
and time intensive, or does not enable continuous monitoring of the system. As
a result, in-service train vibrations have been shown to be a cheaper
alternative for monitoring of railway track systems. In this paper, a method is
proposed to detect different maintenance needs of railway track systems using a
single pass of train direction. The DR-Train dataset that is publicly available
was used. Results show that by using a simple classifier such as the k-nearest
neighbor (k-NN) algorithm, the signal energy features of the acceleration data
can achieve 76\% accuracy on two types of maintenance needs, tamping and
surfacing. The results show that the transverse direction is able to more
accurately detect maintenance needs, and triaxial accelerometer can give
further information on the maintenance needs. Furthermore, this paper
demonstrates the use of multi-label classification to detect multiple types of
maintenance needs simultaneously. The results show multi-label classification
performs only slightly worse than the simple binary classification (72\%
accuracy) and that this can be a simple method that can easily be deployed in
areas that have a history of many maintenance issues.

本论文提出了使用单次列车行驶指标来检测铁路轨道系统不同的维护需求。结果显示，通过使用简单的分类器如 k 最近邻算法，加速度数据的信号能量特征在压实和铺装两种维护需求上能够达到 76% 的准确率。结果还表明，东西向方向能够更准确地检测维护需求，而三轴加速度计能够提供更多的维护需求信息。此外，本论文还展示了使用多标签分类同时检测多种维护需求的方法。结果显示多标签分类的性能略低于简单的二分类（72% 准确率），但这是一种能够轻松部署在存在许多维护问题的地区的简单方法。