Journal
STRUCTURE AND INFRASTRUCTURE ENGINEERING
Volume 17, Issue 12, Pages 1710-1729Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/15732479.2020.1832536
Keywords
Condition monitoring; defect; dynamic axle load; envelope spectrum analysis; flat detection; railway wheel; train-track interaction; weighing-in-motion; wheel flat
Categories
Funding
- CONSTRUCT -Institute of R&D In Structures and Construction - FEDER funds through COMPETE2020 Programa Operacional Competitividade e Internacionalizacao (POCI) [POCI-01-0145-FEDER007457]
- FCT -Fundacao para a Ciencia e a Tecnologia
- project PEDDIR DEMO [NORTE-01-0247-FEDER006397]
- European Commission [691135]
- European funds through the H2020 (SHIFT2RAIL Innovation Programme)
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The research presents an approach using envelope spectrum analysis to detect wheel flats in high axle load train wheelsets. The sensitivity of the method to track unevenness, random wheel flat impact occurrence, and flat severity is discussed and tested, as well as its ability to accurately detect wheel flats under different noise intensities. The results show that envelope spectrum analysis is a cost-effective method capable of detecting wheel flats and flat impact frequency for different train speeds in real-world conditions.
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.
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