Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages SCIE SCOPUS

Cited 12 time in WEB OF SCIENCE Cited 12 time in Scopus
Title
Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages
Author(s)
Kim, Byungmo; Min, Cheonhong; Kim, Hyungwoo; Cho, Sugil; Oh, Jaewon; Ha, Seung-Hyun; Yi, Jin-hak
KIOST Author(s)
Yi, Jin-Hak(이진학)
Alternative Author(s)
이진학
Publication Year
2019-07-12
Abstract
There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.
ISSN
1424-8220
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/575
DOI
10.3390/s19143047
Bibliographic Citation
SENSORS, v.19, no.14, 2019
Publisher
MDPI
Subject
IDENTIFICATION
Keywords
damage detection; cosine similarity; structural health monitoring; system identification; structural integrity assessment
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
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