[연구] 석사과정 이대환, SCIE 논문지(MDPI Sensors/Q1) 게재
- 스마트팩토리융합학과
- 조회수3442
- 2023-07-22
석사과정 이대환 학생(지도교수 : 정종필)의 연구(Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network)가 MDPI Sensors(Impact Factor: 3.9 (2022); 5-Year Impact Factor: 4.1 (2022))에 게재됐다.
DOI: https://doi.org/10.3390/s23146587 or https://www.mdpi.com/1424-8220/23/14/6587
논문요약 - In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault diagnosis. However, at manufacturing sites, it is impossible to have enough training samples to represent all fault types under all operating conditions. In addition, most studies consider only accuracy, and models are complex and computationally expensive. Research that only considers accuracy is inefficient since manufacturing sites change rapidly. Therefore, in this study, we propose a few-shot learning model that can effectively learn with small data. In addition, a Depthwise Separable Convolution layer that can effectively reduce parameters is used together. In order to find an efficient model, the optimal hyperparameters were found by adjusting the number of blocks and hyperparameters, and by using a Depthwise Separable Convolution layer for the optimal hyperparameters, it showed higher accuracy and fewer parameters than the existing model.