基于随机功能池模型的复合材料无人机机翼损伤定位与震级估计

Peiyuan Zhou, Otis Kopsaftopoulos
{"title":"基于随机功能池模型的复合材料无人机机翼损伤定位与震级估计","authors":"Peiyuan Zhou, Otis Kopsaftopoulos","doi":"10.12783/shm2021/36240","DOIUrl":null,"url":null,"abstract":"A vibration-based active-sensing global SHM method is proposed and evaluated for its damage localization and quantification accuracy on complex wing structure. In the process, the wing structure is actuated by a white noise vibration and the response signals are collected by a distributed sensor network. The proposed SHM method first utilize auto-regressive exogenous (ARX) model [1] for representing the time-domain response at each sensor location under various damage conditions, where stochasticity contained in structural response is minimized and identified. ARX models are then mapped to damage parameter space via vector-dependent functionally pooled (VFP) method [2]. Then, a damage estimation algorithm based on minimizing VFP-ARX model prediction error is developed. Finally, the damage estimation results are evaluated as the possibility of leveraging multiple senor signal in SHM process is implicated.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DAMAGE LOCALIZATION AND MAGNITUDE ESTIMATION ON A COMPOSITE UAV WING VIA STOCHASTIC FUNCTIONALLY POOLED MODELS\",\"authors\":\"Peiyuan Zhou, Otis Kopsaftopoulos\",\"doi\":\"10.12783/shm2021/36240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A vibration-based active-sensing global SHM method is proposed and evaluated for its damage localization and quantification accuracy on complex wing structure. In the process, the wing structure is actuated by a white noise vibration and the response signals are collected by a distributed sensor network. The proposed SHM method first utilize auto-regressive exogenous (ARX) model [1] for representing the time-domain response at each sensor location under various damage conditions, where stochasticity contained in structural response is minimized and identified. ARX models are then mapped to damage parameter space via vector-dependent functionally pooled (VFP) method [2]. Then, a damage estimation algorithm based on minimizing VFP-ARX model prediction error is developed. Finally, the damage estimation results are evaluated as the possibility of leveraging multiple senor signal in SHM process is implicated.\",\"PeriodicalId\":180083,\"journal\":{\"name\":\"Proceedings of the 13th International Workshop on Structural Health Monitoring\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th International Workshop on Structural Health Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12783/shm2021/36240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th International Workshop on Structural Health Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12783/shm2021/36240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种基于振动主动感知的全局SHM方法,并对其在复杂机翼结构上的损伤定位和量化精度进行了评价。在此过程中,机翼结构由白噪声振动驱动,响应信号由分布式传感器网络采集。所提出的SHM方法首先利用自回归外生(ARX)模型[1]来表示不同损伤条件下每个传感器位置的时域响应,将结构响应中的随机性最小化并识别出来。然后通过矢量依赖的功能池(vector-dependent functionally pooled, VFP)方法将ARX模型映射到损伤参数空间[2]。然后,提出了一种基于最小化VFP-ARX模型预测误差的损伤估计算法。最后,对损伤估计结果进行了评价,考虑了在SHM过程中利用多传感器信号的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
DAMAGE LOCALIZATION AND MAGNITUDE ESTIMATION ON A COMPOSITE UAV WING VIA STOCHASTIC FUNCTIONALLY POOLED MODELS
A vibration-based active-sensing global SHM method is proposed and evaluated for its damage localization and quantification accuracy on complex wing structure. In the process, the wing structure is actuated by a white noise vibration and the response signals are collected by a distributed sensor network. The proposed SHM method first utilize auto-regressive exogenous (ARX) model [1] for representing the time-domain response at each sensor location under various damage conditions, where stochasticity contained in structural response is minimized and identified. ARX models are then mapped to damage parameter space via vector-dependent functionally pooled (VFP) method [2]. Then, a damage estimation algorithm based on minimizing VFP-ARX model prediction error is developed. Finally, the damage estimation results are evaluated as the possibility of leveraging multiple senor signal in SHM process is implicated.
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
自引率
0.00%
发文量
0
期刊最新文献
NONLINEAR BULK WAVE PROPAGATION IN A MATERIAL WITH RANDOMLY DISTRIBUTED SYMMETRIC AND ASYMMETRIC HYSTERETIC NONLINEARITY SPATIAL FILTERING TECHNIQUE-BASED ENHANCEMENT OF THE RECONSTRUCTION ALGORITHM FOR THE PROBABILISTIC INSPECTION OF DAMAGE (RAPID) KOOPMAN OPERATOR BASED FAULT DIAGNOSTIC METHODS FOR MECHANICAL SYSTEMS ON THE APPLICATION OF VARIATIONAL AUTO ENCODERS (VAE) FOR DAMAGE DETECTION IN ROLLING ELEMENT BEARINGS INTELLIGENT IDENTIFICATION OF RIVET CORROSION ON STEEL TRUSS BRIDGE BY SINGLE-STAGE DETECTION NETWORK
×
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