[연구] 석사과정 이서영, SCI 논문지(MDPI Electronics/Q2) 게재
- 스마트팩토리융합학과
- 조회수6026
- 2022-05-07
석사과정 이서영 학생(지도교수 : 정종필)의 연구(SSA-SL Transformer for Bearing Fault Diagnosis under Noisy Factory Environments)가 MDPI Electronics(Impact Factor: 2.397 (2020); 5-Year Impact Factor: 2.408 (2020))에 게재됐다.
https://doi.org/10.3390/electronics11091504 / https://www.mdpi.com/2079-9292/11/9/1504
논문요약 - Among the smart factory studies, we describe defect detection research conducted on bearings, which are elements of mechanical facilities. Bearing research has been consistently conducted in the past; however, most of the research has been limited to using existing artificial intelligence models. In addition, previous studies assumed the factories situated in the bearing defect research were insufficient. Therefore, a recent research was conducted that applied an artificial intelligence model and the factory environment. The transformer model was selected as state-of-the-art (SOTA) and was also applied to bearing research. Then, an experiment was conducted with Gaussian noise applied to assume a factory situation. The swish-LSTM transformer (Sl transformer) framework was constructed by redesigning the internal structure of the transformer using the swish activation function and long short-term memory (LSTM). Then, the data in noise were removed and reconstructed using the singular spectrum analysis (SSA) preprocessing method. Based on the SSA-Sl transformer framework, an experiment was performed by adding Gaussian noise to the Case Western Reserve University (CWRU) dataset. In the case of no noise, the Sl transformer showed more than 95% performance, and when noise was inserted, the SSA-Sl transformer showed better performance than the comparative artificial intelligence models.