Crack detection using machine learning algorithm = 기계학습 알고리즘을 활용한 크랙 진단

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 27
  • Download : 0
In this paper, we propose a crack detection method using finite element model and machine learning algorithm. However, in order to use machine learning for crack detection, a lot of data is needed. Therefore, in this study, we propose a crack detection method that efficiently generates deformation data corresponding to various cracks using XFEM and generates a corresponding crack image when deformation data is given. To do this, we use the structure of variational autoencoder (VAE), which is a representative model, and modified the loss function to fit the problem. The crack detection results show that the position and shape of the crack can be detected using the deformation data. The crack detection method using the mode shape independent of the load is expected to be a basic study of the crack detection using the vibration data.
Advisors
Lee, Phill-Seungresearcher이필승researcher
Description
한국과학기술원 :기계공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 기계공학과, 2019.2,[iii, 46 p. :]

Keywords

기계학습▼a균열 진단▼aXFEM▼a생성 모델▼aVAE; machine learning▼acrack detection▼aXFEM▼agenerative model▼aVAE

URI
http://hdl.handle.net/10203/265931
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843044&flag=dissertation
Appears in Collection
ME-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0