SHAP를 활용한 중요변수 파악 및 선택에 따른 잔여유효수명 예측 성능 변동에 대한 연구

• 영문명: A Study on the Remaining Useful Life Prediction Performance Variation based on Identification and Selection by using SHAP
• 발행기관: 한국산업경영시스템학회
• 저자명: 윤연아(Yeon Ah Yoon) 이승훈(Lee Seung Hoon) 김용수(Yong Soo Kim)
• 간행물 정보: 『산업경영시스템학회지』제44권 제4호, 1~11쪽, 전체 11쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.12.31

영문 초록

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 manage-ment (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.

목차

Ⅰ. 서론

Ⅱ. 관련문헌 연구

Ⅲ. 잔여유효수명 예측 및 SHAP을 통한 변수 기여도 분석프로세스

Ⅳ. 실험결과 및 분석

Ⅴ. 결론 및 추후 연구과제

Acknowledgement

References

키워드

Remaining Useful Life Explainable Artificial Intelligence Shapley Additive Explanations Feature Selection Deep Learning