通过基于振动图像表示的数据融合增强轨道损伤识别能力

IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Journal of Nondestructive Evaluation Pub Date : 2023-12-19 DOI:10.1007/s10921-023-01028-7
Shaohua Wang, Lihua Tang, Yinling Dou, Zhaoyu Li, Kean C. Aw
{"title":"通过基于振动图像表示的数据融合增强轨道损伤识别能力","authors":"Shaohua Wang,&nbsp;Lihua Tang,&nbsp;Yinling Dou,&nbsp;Zhaoyu Li,&nbsp;Kean C. Aw","doi":"10.1007/s10921-023-01028-7","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation\",\"authors\":\"Shaohua Wang,&nbsp;Lihua Tang,&nbsp;Yinling Dou,&nbsp;Zhaoyu Li,&nbsp;Kean C. Aw\",\"doi\":\"10.1007/s10921-023-01028-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.</p></div>\",\"PeriodicalId\":655,\"journal\":{\"name\":\"Journal of Nondestructive Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nondestructive Evaluation\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10921-023-01028-7\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10921-023-01028-7","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

摘要 本文介绍了基于振动的图像表示和数据融合在特征提取方面的独特优势,为铁路工程中的损伤识别提供了卓越的性能。具体来说,基于车辆-轨道耦合动力学,生成了不同紧固件损伤条件下的轨道振动数据集。在条件变异自动编码器(CVAE)的帮助下,通过将一维振动信号转换为带有递归图(RPs)的二维灰度图像,将加速度 RPs 和位移 RPs 融合以增强特征提取。结果表明,检测基于振动图像的纹理模式和颜色分布的变化有助于有效识别损坏,降低损坏识别对轨道不规则性恶化的敏感性。结果表明,具有准静态特征的位移 RPs 更适用于紧固件损坏识别。此外,通过结合加速度 RPs 的随机动态特征和位移 RPs 的准静态特征进行数据融合,可以扩大测量范围的容差,从而准确识别紧固件损坏。在测试了不同的采样频率和附加噪声后,验证了所提出方法的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Nondestructive Evaluation
Journal of Nondestructive Evaluation 工程技术-材料科学:表征与测试
CiteScore
4.90
自引率
7.10%
发文量
67
审稿时长
9 months
期刊介绍: Journal of Nondestructive Evaluation provides a forum for the broad range of scientific and engineering activities involved in developing a quantitative nondestructive evaluation (NDE) capability. This interdisciplinary journal publishes papers on the development of new equipment, analyses, and approaches to nondestructive measurements.
期刊最新文献
The Detection of Local Impact Fatigue Damage on Metal Materials by Combining Nonlinear Acoustic Modulation and Coda Wave Interferometry Feasibility Study on the Use of the Coplanar Capacitive Sensing Technique for Underwater Non-Destructive Evaluation A Method for Semi-automatic Mode Recognition in Acoustic Emission Signals Incipient Near Surface Cracks Characterization and Crack Size Estimation based on Jensen–Shannon Divergence and Wasserstein Distance Adaptive and High-Precision Isosurface Meshes from CT Data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1