Open Access

ARTICLE

Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep-Learning Techniques

Xianxian Yin¹, Shimin Yin¹, Yiming Bu², Xiukun Wei^3,*

1 School of Transportation Engineering, Shandong Jianzhu University, Jinan, 250101, China
2 China Construction Eighth Bureau (Shandong) Design Consulting Co., Ltd., Jinan, 250100, China
3 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

* Corresponding Author: Xiukun Wei. Email:

(This article belongs to the Special Issue: Advanced Computer Vision Methods and Related Technologies in Structural Health Monitoring)

Structural Durability & Health Monitoring 2024, 18(5), 623-640. https://doi.org/10.32604/sdhm.2024.050195

Received 30 January 2024; Accepted 28 April 2024; Issue published 19 July 2024

Abstract

As an important component of load transfer, various fatigue damages occur in the track as the rail service life and train traffic increase gradually, such as rail corrugation, rail joint damage, uneven thermite welds, rail squats fastener defects, etc. Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network, and the coexistence of the above-mentioned typical track defects in the track system is considered. Firstly, the dynamic relationship between the track defects (using the example of the fastening defects) and the axle-box vibration acceleration (ABVA) is investigated using the dynamic vehicle-track model. Then, a simulation model for the coupled dynamics of the vehicle and track with different track defects is established, and the wavelet power spectrum (WPS) analysis is performed for the vibration acceleration signals of the axle box to extract the characteristic response. Lastly, using wavelet spectrum photos as input, an automatic detection technique based on the deep convolution neural network (DCNN) is suggested to realize the real-time intelligent detection and identification of various track problems. The findings demonstrate that the suggested approach achieves a 96.72% classification accuracy.

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Keywords

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