Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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A study on Data Preprocessing for Developing Remaining Useful Life Predictions based on Stochastic Degradation Models Using Air Craft Engine Data

항공엔진 열화데이터 기반 잔여수명 예측력 향상을 위한 데이터 전처리 방법 연구

  • Yoon, Yeon Ah (Department of Industrial and Management, Kyonggi University Graduate School) ;
  • Jung, Jin Hyeong (Department of Industrial and Management, Kyonggi University Graduate School) ;
  • Lim, Jun Hyoung (Intelligent System Engineering Division, Hancom MDS) ;
  • Chang, Tai-Woo (Department of Industrial and Management, Kyonggi University) ;
  • Kim, Yong Soo (Department of Industrial and Management, Kyonggi University)
  • 윤연아 (경기대학교 일반대학원 산업경영공학과) ;
  • 정진형 (경기대학교 일반대학원 산업경영공학과) ;
  • 임준형 (한컴MDS Intelligent System Engineering 사업본부) ;
  • 장태우 (경기대학교 산업경영공학과) ;
  • 김용수 (경기대학교 산업경영공학과)
  • Received : 2020.04.13
  • Accepted : 2020.06.17
  • Published : 2020.06.30
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Abstract

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.

Keywords

References

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