Many freight waggons in Europe have been recently equipped with embedded systems (ESs) for vehicle tracking. This provides opportunities to implement the real-time fault diagnosis algorithm on ESs without additional investment. In this paper, we design a 1D lightweight Convolutional Neural Network (CNN) architecture, i.e. LightWFNet, guided by Bayesian Optimization for wheel flat (WF) detection. We tackle two main challenges. (1) Carbody acceleration has to be used for WF detection, where signal-to-noise ratio is much lower than at axle box level and thus the WF detection is much more difficult. (2) ESs have very limited computation power and energy supply. To verify the proposed LightWFNet, the field data measured on a tank waggon under operational condition are used. In comparison to the state-of-the-art lightweight CNNs, LightWFNet is validated for WF detection by using carbody accelerations with much lower computational costs.

Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing low-cost inertial sensor-based measurement systems provide reliable measurements of track geometry in vertical directions. However, solutions are needed for track geometry parameter measurement in the lateral direction. In this research, the authors developed a visual measurement system for track gauge evaluation. It involves the detection of measurement points and the visual measurement of the distance between them. The accuracy of the visual measurement system was evaluated in the laboratory and showed promising results. The initial field test was performed in the Vilnius railway station yard, driving at low velocity on the straight track section. The results show that the image point selection method developed for selecting the wheel and rail points to measure distance is stable enough for TG measurement. Recommendations for the further improvement of the developed system are presented.

This study focuses on the quantification of the influence of rolling stock failures (RSFs) on railway infrastructure. Taking the wheel flat, a common RSF, as an example, we introduce four quantification indexes to evaluate the influence on the following four deterioration mechanisms: track settlement (TS), track component fatigue (TCF), abrasive wear (AW), and rolling contact fatigue (RCF). Our results indicate that TS, TCF, and AW increase sharply with the increase of the wheel flat length and the vehicle speed, and this increasing trend becomes more acute with the increase of the wheel flat length and the vehicle speed. At low speeds, RCF increases gradually as the wheel flat length increases; at high speeds, it increases sharply at first and then decreases gradually. The influence of the wheel flat on TCF and AW is the most obvious, followed by TS and RCF. These findings can help infrastructure managers (IMs) to better understand infrastructure conditions related to RSFs and can aid them in managing problems with vehicle abnormality in track access charging.