Journal of Aerospace System Engineering (항공우주시스템공학회지)

The Society for Aerospace System Engineering (항공우주시스템공학회)
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Neural Network based Aircraft Engine Health Management using C-MAPSS Data

C-MAPSS 데이터를 이용한 항공기 엔진의 신경 회로망 기반 건전성관리

  • Yun, Yuri (Hyundai Construction Equipment) ;
  • Kim, Seokgoo (Department of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Cho, Seong Hee (Department of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Choi, Joo-Ho (School of Aerospace and Mechanical Engineering, Korea Aerospace University)
  • 윤유리 (현대건설기계 신뢰성 평가부) ;
  • 김석구 (한국 항공대학교 항공 우주 및 기계공학과) ;
  • 조성희 (한국 항공대학교 항공 우주 및 기계공학과) ;
  • 최주호 (한국 항공대학교 항공 우주 및 기계공학부)
  • Received : 2018.12.10
  • Accepted : 2019.11.05
  • Published : 2019.12.31
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Abstract

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

항공기 엔진의 고장예지 및 건전성 관리(PHM)는 고장 또는 수명한계 도달 전에 잔존 유효 수명을 예측하는 것이다. PHM 기술 중 예측모델을 확립하는 방법은 물리 기반과 데이터 기반 방법이 있다. 물리기반 방법은 적은 데이터로 정확한 예측이 가능하지만 확립된 손상 물리 모델이 적어서 적용에 한계가 있다. 본 연구는 따라서 데이터 기반 방법을 적용하였으며, 수명 예측을 위해서 신경회로망 알고리즘 중 Multi-layer Perceptron을 이용하였다. 이를 위해 미국 항공우주국(NASA)에서 개발한 C-MAPSS 코드로 생성된 가상 데이터 세트를 이용하여 신경회로망을 학습하였다. 학습된 신경회로망 모델은 테스트 세트에 적용한 후 잔존 유효 수명의 신뢰구간을 예측하고 실제 값을 통해 정확도를 검증하였다. 또한 본 연구에서 제시된 방법을 기존 문헌의 것과도 비교하였고 그 결과 비교적 양호한 정확도를 확인할 수 있었다.

Keywords

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