Approaches for weigh-in-motion and wheel defect detection of railway vehicles

• Type: Chapter or Part of a Book
• Date: 2022

Authors

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

Book

Title: Rail Infrastructure Resilience: A Best-Practices Handbook

Pages: 183-207

ISBN: 978-012821042-0, 978-012821043-7

Indexing

Scopus

Other information

Authenticus ID: P-00X-6G6

DOI: 10.1016/b978-0-12-821042-0.00009-5

Abstract (EN): The development of efficient weigh-in-motion and wheel defect detection methods with high accuracy estimation procedures from track measurements is one of the major subjects that draw the attention of both the railway industry and scientific researchers. This information triggers a warning in the administration system when a train is overloaded or operating under abnormal conditions. The main novel aspect of this study is to define a methodology to obtain weigh the train in motion and allows the identification of a wheel flat using a wayside monitoring system. To achieve this, two approaches were proposed to obtain an estimation of the wheel static load as well as distinguish the healthy wheel from the defective one in order to allow the system to activate the necessary alerts. A wide range of numerical simulations based on a train-track interaction model has been performed for different train speeds. From the obtained results, it is evident that the proposed approaches are capable tools and cost-effective methods to estimate the wheel static load as well as an abnormal condition of rolling stock. © 2022 Elsevier Inc. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific