@inproceedings{c1fcfd25e69e4eb7b867ba3626bb4e91,
title = "A Deep Learning Model for Predicting Damaged Points via Random Vibration Signal Analysis",
abstract = "Structural health monitoring is an area of growing interest and is worthy of new and innovative approaches. Since the automatic diagnosis of structures is very complex and challenging, recent research to apply deep learning techniques has been actively conducted. In this study, we assumed that a PLA beam copied by 3D printing is the smallest unit constituting a complex structure and applied GRU to detect defects. To set the defect point of the beam, a total of four holes were drilled at regular intervals, and then a mass was attached. Signals at different locations were collected through a vibrator and trained through GRU, and the results were compared in terms of RMSE value. As a result of this experiment, we checked the defect by inputting test data into the trained model and were able to measure the defect degree of the PLA beam with a weighted average F1 score of 84%.",
keywords = "Deep learning, GRU, Output only signal, Structural health monitoring, Vibration analysis",
author = "Matthew Sands and Jongyeop Kim and Jinki Kim and Seongsoo Kim",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 24th IEEE/ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022 ; Conference date: 07-12-2022 Through 09-12-2022",
year = "2022",
doi = "10.1109/SNPD54884.2022.10051778",
language = "English",
series = "Proceedings - 2022 IEEE/ACIS 24th International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "75--82",
editor = "Shu-Ching Chen and Her-Terng Yau and Roland Stenzel and Hsiung-Cheng Lin",
booktitle = "Proceedings - 2022 IEEE/ACIS 24th International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2022",
address = "United States",
}