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권숙희,김재훈,손석만,이성덕,Kwon, Sukhui,Kim, Jaehoon,Sohn, SeokMan,Lee, SungDuck 한국통계학회 2021 응용통계연구 Vol.34 No.4
본 연구에서는 여러가지 시계열 모형 중 평활법(가법계절지수, 승법계절지수), 계절 ARIMA 모형, ARARCH 그리고 AR-GARCH 회귀모형을 이용하여 최대 전력수요를 예측하는 방법을 연구하였다. 이 때 가중 평균모형으로 추세를 갖는 시계열 모형과 온도에 대한 회귀 모형을 적절한 가중치로 예측 정확도를 높이는 방법도 연구하였다. 결과적으로 AR-GARCH 회귀모형으로 예측하는 것이 가중 우수함을 보였다. Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.
진동신호 특성 예측 및 분류를 통한 회전체 고장진단 방법
김동환(Donghwan Kim),손석만(Seokman Sohn),김연환(Yeonwhan Kim),배용채(Yongchae Bae) 한국소음진동공학회 2014 한국소음진동공학회 학술대회논문집 Vol.2014 No.10
In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm’s performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.