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。

利用运行中的列车振动检测铁路维护需求

Using In-Service Train Vibration for Detecting Railway Maintenance Needs

Bike-sharing systems have emerged as a significant element of urban mobility,
providing an environmentally friendly transportation alternative. With the
increasing integration of electric bikes alongside mechanical bikes, it is
crucial to illuminate distinct usage patterns and their impact on maintenance.
Accordingly, this research aims to develop a comprehensive understanding of
mobility dynamics, distinguishing between different mobility modes, and
introducing a novel predictive maintenance system tailored for bikes. By
utilising a combination of trip information and maintenance data from
Barcelona's bike-sharing system, Bicing, this study conducts an extensive
analysis of mobility patterns and their relationship to failures of bike
components. To accurately predict maintenance needs for essential bike parts,
this research delves into various mobility metrics and applies statistical and
machine learning survival models, including deep learning models. Due to their
complexity, and with the objective of bolstering confidence in the system's
predictions, interpretability techniques explain the main predictors of
maintenance needs. The analysis reveals marked differences in the usage
patterns of mechanical bikes and electric bikes, with a growing user preference
for the latter despite their extra costs. These differences in mobility were
found to have a considerable impact on the maintenance needs within the
bike-sharing system. Moreover, the predictive maintenance models proved
effective in forecasting these maintenance needs, capable of operating across
an entire bike fleet. Despite challenges such as approximated bike usage
metrics and data imbalances, the study successfully showcases the feasibility
of an accurate predictive maintenance system capable of improving operational
costs, bike availability, and security.

鉴于近年来电动自行车与机械自行车的增加，本研究旨在开发一种全面了解移动动态的方法，区分不同的移动方式，并引入一种专为自行车量身定制的新型预测性维护系统。通过利用巴塞罗那的自行车共享系统 Bicing 的行程信息和维护数据，本研究对移动模式及其与自行车部件故障之间的关系进行了广泛分析，并应用了统计和机器学习生存模型来准确预测自行车的维护需求。