[연구] 석사과정(졸업생) 김동현, SCI 논문지(MDPI applied sciences/Q2) 게재
- 스마트팩토리융합학과
- 조회수5491
- 2022-06-09
석사과정(졸업생) 김동현 학생(지도교수 : 정종필)의 연구(SSA-CAE-Based Abnormal Data Classification Method in Edge Intelligence Device of CNC Machine)가 MDPI applied sciences(Impact Factor: 2.679 (2020); 5-Year Impact Factor: 2.736 (2020))에 게재됐다.
https://doi.org/10.3390/app12125864 / https://www.mdpi.com/2076-3417/12/12/5864
논문요약 - Smart factories and big data are important factors in the Fourth Industrial Revolution. Smart factories aim for automation and integration; however, the most important part is the application of data. Despite extensive research on the maintenance and quality management of big data-based production equipment, industrial data gathered for analysis contain more normal data than abnormal data. In addition, a significant amount of energy is expended in the data pre-processing process to analyze the acquired data. Therefore, to maintain production equipment and quality management, data classification technology that allows easy data analysis by classifying abnormal data into normal data is required. In this paper, we propose an abnormal data classification architecture for cycle data sets gathered from production facilities through SSA-CAE along with data storage methods for each product unit. SSA-CAE is a hybrid technique that combines singular spectrum analysis (SSA) techniques that are effective in reducing noise in time series data with convolutional auto encoder (CAE) that have performed well in time series.