Damage identification of sandwich panel with truss core by integrating deep learning technique

IMECH-IR > 流固耦合系统力学重点实验室

	Damage identification of sandwich panel with truss core by integrating deep learning technique
	Wang YW(王一伟); Lu LL(路玲玲); Song HW(宋宏伟); Chen WM(陈伟民); Huang CG(黄晨光)
会议录名称	Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
	2019
页码	2161-2168
会议名称	12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
会议日期	September 10, 2019 - September 12, 2019
会议地点	Stanford, CA, United states
摘要	This paper reports some preliminary work in IMECH, CAS on integrating deep learning technique to identify internal damages in the sandwich panel with truss core. Cell-missing and unbound nodes are two main types of damages to be concerned. Numerical models of panels with various damage features are presented to provide original structural dynamic responses, and a vibration-based damage index is proposed to extract damage features, therefore image datasets of randomly damaged samples are constructed for training and testing. The training and testing processes are conducted by Faster R-CNN (regions with convolutional neural network). Results reveal that the proposed method can identify damage location and extent in reasonable precision. © 2019 by DEStech Publications, Inc. All rights reserved.
ISBN号	9781605956015
URL	查看原文
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/85096
专题	流固耦合系统力学重点实验室
作者单位	1.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China 2.Institute of Mechanics, Chinese Academy of Sciences, Institute of Mechanics, Chinese Academy of Sciences, China
推荐引用方式 GB/T 7714	Wang YW,Lu LL,Song HW,et al. Damage identification of sandwich panel with truss core by integrating deep learning technique[C]Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring,2019:2161-2168.