Using In-Service Train Vibration for Detecting Railway Maintenance Needs

TL;DR

This paper presents a cost-effective method using in-service train vibrations and simple classifiers to detect railway maintenance needs accurately, enabling continuous monitoring with minimal additional equipment.

Contribution

It introduces a novel approach utilizing in-service train vibrations and multi-label classification for real-time railway maintenance detection.

Findings

Achieved 76% accuracy in detecting maintenance needs using acceleration signal energy features.

Transverse acceleration direction provides more accurate maintenance detection.

Multi-label classification performs nearly as well as binary classification, enabling detection of multiple issues simultaneously.

Abstract

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.