Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear

Abstract

The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of wheel flange thickness to an extreme level increases the probability of train accidents. To avoid the unsafe operation of a rail vehicle, it is important to stay continuously up to date on the status of the wheel flange thickness dimensions by using precise and accurate measurement tools. The wheel wear measurement tools that are based on laser and vision technology are quite expensive to implement in railway lines of developing countries. Alternatively significant measurement errors can result from using imprecise measurement tools such as the hand tools, which are currently utilized by the railway companies such as Addis Ababa Light Rail Transit Service (AALRTS). Thus, the objective of this research is to propose and test a new measurement tool that uses an inductive displacement sensor. The proposed system works in both static and dynamic state of the railway vehicle and it is able to save the historical records of the wheel flange thickness for further analysis. The measurement system is fixed on the bogie frame. The fixture was designed using dimensions of the bogie and wheelset structure of the trains currently used by AALRTS. Laboratory experiments and computer simulations for of the electronic system were carried out to assess the feasibility of the data acquisition and analysis method. The noises and unwanted signals due to the dynamics of the system are filtered out from the sensor readings. The results show that the implementation of the proposed measurement system can accurately measure the wheel flange wear. Also, the faulty track section can be identified using the system recorded data and the operational control center data.

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

“…Another endeavor was carried out using an onboard measurement system for identifying wheel flange wear. This proposed an inductive displacement sensor and successfully located wheel wear due to false flanges [32]. Authors in [33] proposed an algorithm for an adaptive, multiscale, morphological filtering method for extracting the information about wheel defects, such as wheel flats, from the vehicle axle box vibration signal, which also accommodates track condition information.…”

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

“…For pro-active maintenance purposes, Pacifique Turabimana and Celestin Nkundineza propose in their article [ 28 ] the testing of a new measurement tool that employs an inductive displacement sensor. The proposed system is said to be working in both static and dynamic state of the railway vehicle and being able to save the historical records of the wheel flange thickness for further analysis.…”

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

“…In the railway wear monitoring literature [3][4][5][6], two main categories of monitoring approaches can be found, namely direct and indirect approaches. The direct approaches are mainly based on the use of digital photography to monitor specific locations on the rail [7]. Images are captured using optical devices such as high-speed cameras, after which conventional image processing tools and algorithms are applied to extract directly the information required concerning deterioration caused by the wear process [8,9].…”