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%.