An approach for wheel flat detection of railway train wheels using envelope spectrum analysis

• Type: Article in International Scientific Journal
• Date: 2020

Authors

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

Journal

Title: Structure and Infrastructure Engineering

Pages: 1-20

ISSN: 1573-2479

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00S-W11

DOI: 10.1080/15732479.2020.1832536

Abstract (EN): Due to the increasing demand for safer and faster rail transport, train wheelsets operating under high axle loads require more careful and reliable inspections and maintenance. During service, the train wheelsets are constantly operating under harsh conditions such as fatigue, thermal variation and impact. The wheel defects can induce damage to railway tracks or even derailments, increasing costs for both railway administrations and rolling stock operators. Therefore, early detection of wheel defects may prevent significant damages that could lead to service interruptions or derailments. The purpose of this research is to present an approach to detect the presence of wheel flats using an envelope spectrum analysis, as well as to test, discuss and analyse the sensitivity of the proposed approach to the unevenness of the track, the random position of the wheel flat impact occurrence and the severity of the flat. Subsequently, the sensitivity of the proposed method is tested to accurately detect a wheel flat when the signal is perturbed by different noise intensities. A wide range of 3D simulations based on a train-track interaction model has been performed for different train speeds and different types of flat geometries. From the obtained results, it is evident that envelope spectrum analysis is a capable tool and a cost-effective method that can be used to detect wheel flats along with the flat impact frequency for different train speeds in real-world conditions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20