Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

1. 서 론
2. 관련문헌 연구
3. 잔여유효수명 예측 및 SHAP을 통한 변수기여도 분석프로세스
    3.1 데이터 전처리
    3.2 잔여유효수명 예측
    3.3 SHAP을 활용한 중요변수 파악
    3.4 중요변수 선택에 따른 잔여유효수명 예측 성능변동 확인
4. 실험결과 및 분석
    4.1 데이터 소개
    4.2 데이터 전처리 및 데이터 준비
    4.3 잔여수명 예측 결과
    4.4 SHAP을 통한 중요변수 파악
    4.5 변수 선택에 따른 잔여수명 예측 성능 변동확인
5. 결론 및 추후 연구과제
References

• Yeon Ah Yoon(경기대학교 일반대학원 산업경영공학과) | 윤연아
• Lee Seung Hoon(경기대학교 일반대학원 산업경영공학과) | 이승훈
• Yong Soo Kim(경기대학교 산업경영공학과) | 김용수 Corresponding Author