항공엔진 열화데이터 기반 잔여수명 예측력 향상을 위한 데이터 전처리 방법 연구

• 영문명: A study on Data Preprocessing for Developing Remaining Useful Life Predictions based on Stochastic Degradation Models Using Air Craft Engine Data
• 발행기관: 한국산업경영시스템학회
• 저자명: 윤연아(Yeon Ah Yoon) 정진형(Jin Hyeong Jung) 임준형(Jun Hyoung Lim) 장태우(Tai-Woo Chang) 김용수(Yong Soo Kim)
• 간행물 정보: 『산업경영시스템학회지』제43권 제2호, 48~55쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2020.06.30

영문 초록

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

목차

1. 연구 개요

2. 관련 문헌 연구

3. 연구 프로세스

4. 실험분석 및 결과

5. 결론 및 추후 연구과제

Acknowledgement

References

키워드

Remaining Useful Life Degradation Preprocessing Machine Learning Sliding Window