앙상블 모델 기반의 기계 고장 예측 방법

• 영문명: An Ensemble Model for Machine Failure Prediction
• 발행기관: 한국산업경영시스템학회
• 저자명: 천강민(Kang Min Cheon) 양재경(Jaekyung Yang)
• 간행물 정보: 『산업경영시스템학회지』제43권 제1호, 123~131쪽, 전체 9쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2020.03.31

영문 초록

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.

목차

1. 서 론

2. 기존 연구

3. 연구 방법

4. 실험 결과

5. 결 론

References

키워드

Ensemble Model Anomaly Detection STL and GESD Clustering Failure Prediction