Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Araliya Mosleh (Author) FEUP; Pedro Aires Montenegro (Author) FEUP; Pedro Alves Costa (Author) Other; Rui Calçada (Author) FEUP

Journal

Title: Applied Sciences

Vol. 11

Final page: 4002

Publisher: MDPI

Indexing

ISI Web of Knowledge - 33 Citations

Scopus - 37 Citations

Other information

Authenticus ID: P-00T-YWC

DOI: 10.3390/app11094002

Abstract (EN): The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 25