Fractal-based manifold learning for structure health monitoring

Nan Xu, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Structure Health Monitoring (SHM) has been widely used in various engineering fields to ensure the safety of structures. Many of SHM methods are available based on machine learning to recognize the damage pattern, which are very time-consuming. A great challenge for most existing machine learning techniques is that their performances decrease as number of sensors increased for structure under analysis. In this paper, a new SHM technique, integrating manifold learning and fractal analysis, is proposed to detect structural damage. Both temporal and spatial features will be represented in a low dimensional embedding through dimensionality reduction. There are two procedures of the proposed method: temporal dimension reduction by fractal analysis, and spatial dimension reduction by manifold learning (Uniform Manifold Approximation and Projection-UMAP). The proposed methodology is applied to classify seven damage scenarios of benchmark study. The results showed high accuracy to classify different benchmark scenarios and can be potentially used for structure analysis which requires large number of sensors.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-7
Number of pages7
ISBN (Print)9781624106095
DOIs
StatePublished - 2021
Externally publishedYes
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: Jan 11 2021Jan 15 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period1/11/211/15/21

ASJC Scopus subject areas

  • Aerospace Engineering

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