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Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network
Dan Li1; Yang Wang1; Wang Ji Yan2; Wei Xin Ren3
2021-07-01
Source PublicationStructural Health Monitoring
ISSN1475-9217
Volume20Issue:4Pages:1563-1582
Abstract

This study focuses on the acoustic emission wave classification for the sake of more accurate and comprehensive rail crack monitoring in the field typically with complex cracking conditions, high-operational noise, and mass data. There are mainly three types of acoustic emission waves induced by operational noise, impact, and crack propagation, respectively. Synchrosqueezed wavelet transform was introduced to represent intrinsic characteristics of acoustic emission waves more clearly in the time–frequency domain, where acoustic emission waves induced by different mechanisms were found to show various patterns of energy distribution. Then, a multi-branch convolutional neural network model with two branches was developed to automatically classify the three types of acoustic emission waves by taking into account their synchrosqueezed wavelet transform plots in various time–frequency scales. Training, validation, and test data sets were constructed using acoustic emission waves collected through a series of field and laboratory tests with various noise levels and loading conditions. The transfer learning was used to train the model faster, and the Bayesian optimization algorithm was applied to tune the hyperparameters. Finally, the multi-branch convolutional neural network model achieved higher accuracy and robustness than the traditional convolutional neural network model of single branch in identifying different acoustic emission mechanisms. The proposed acoustic emission wave classification method based on synchrosqueezed wavelet transform and multi-branch convolutional neural network is able to detect not only surface rail cracks, where both impact-induced and crack propagation-induced acoustic emission waves would be identified, but also internal rail cracks where only crack propagation-induced acoustic emission waves would be captured.

KeywordRail Crack Monitoring Acoustic Emission Classification Synchrosqueezed Wavelet Transform Multi-branch Convolutional Neural Network
DOI10.1177/1475921720922797
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Instruments & Instrumentation
WOS SubjectEngineering, Multidisciplinary ; Instruments & Instrumentation
WOS IDWOS:000537175500001
PublisherSAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
Scopus ID2-s2.0-85085882687
Fulltext Access
FWCI5.88286
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorWei Xin Ren
Affiliation1.School of Civil Engineering,Hefei University of Technology,Hefei,China
2.State Key Laboratory of Internet of Things for Smart City,Department of Civil and Environmental Engineering,University of Macau,Macao
3.College of Civil and Transportation Engineering,Shenzhen University,Shenzhen,China
Recommended Citation
GB/T 7714
Dan Li,Yang Wang,Wang Ji Yan,et al. Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network[J]. Structural Health Monitoring,2021,20(4):1563-1582.
APA Dan Li,Yang Wang,Wang Ji Yan,&Wei Xin Ren.(2021).Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network.Structural Health Monitoring,20(4),1563-1582.
MLA Dan Li,et al."Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network".Structural Health Monitoring 20.4(2021):1563-1582.
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