Local Track Irregularity Identification Based on Multi-Sensor Time–Frequency Features of High-Speed Railway Bridge Accelerations

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities ba...

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Bibliographic Details
Published in:Sustainability 2023-05, Vol.15 (10), p.8237
Main Authors: Mo, Ye, Zhuo, Yi, Li, Shunlong
Format: Article
Language:English
Subjects:
Algorithms
Bridges
Comparative studies
Computer simulation
Continuous bridges
Continuous wavelet transform
High speed rail
High speed trains
Irregularities
Mathematical models
Numerical analysis
Railroads
Railway engineering
Sensors
Simulation methods
Sustainability
Time-frequency analysis
Traffic speed
Wavelet transforms
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Summary:Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on the multi-sensor time–frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) was used to analyze the multi-sensor accelerations of railway bridges. Moreover, time–frequency features based on the sum of wavelet coefficients were proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features were utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train was adopted as a numerical simulation. Comparative studies were conducted to investigate the influence of vehicle speeds and the location of local track irregularity on the algorithm. Numerical simulation results show that the proposed algorithm can detect and locate local track irregularity accurately and is robust to vehicle speeds.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15108237