There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

1. 서 론
2. 기존 연구
3. 연구 방법
    3.1 전처리(Preprocess)
    3.2 학습데이터셋(Training Data Set) 구성
    3.3 주요속성 추출 및 위험 스코어 예측
    3.4 모델 검증
4. 실험 결과
    4.1 전처리 결과
    4.2 학습 데이터셋 구성 결과
    4.3 주요 속성 추출 및 위험스코어 예측 결과
5. 결 론
References

• Kang Min Cheon(효성인포메이션시스템) | 천강민
• Jaekyung Yang(전북대학교 산업시스템공학과) | 양재경 Corresponding Author