Article
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Preserved in Portico This version is not peer-reviewed
Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
Version 1
: Received: 2 August 2023 / Approved: 3 August 2023 / Online: 3 August 2023 (10:47:27 CEST)
A peer-reviewed article of this Preprint also exists.
He, M.; Qing, W.; Qu, J. Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors. Appl. Sci. 2023, 13, 9663. He, M.; Qing, W.; Qu, J. Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors. Appl. Sci. 2023, 13, 9663.
Abstract
Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.
Keywords
DAS; ballastless track; defects recognition; XGBoost; ResNet
Subject
Engineering, Transportation Science and Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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