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Korea Society for Naval Science and Technology - Vol. 4 , No. 1

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[ Article ]
Journal of the KNST - Vol. 4, No. 1, pp. 78-83
Abbreviation: KNST
ISSN: 2635-4926 (Print)
Print publication date 31 Mar 2021
Received 23 Jan 2021 Revised 02 Feb 2021 Accepted 23 Feb 2021
DOI: https://doi.org/10.31818/JKNST.2021.03.4.1.78
심층신경망과 피어슨 상관계수를 활용한 터보팬 엔진 잔여유효수명 예측
홍창우¹^{, *} ; 허견²

1해군본부 소령/연세대학교 전기전자공학부 박사과정

2연세대학교 전기전자공학부 교수
Prognosis of the Remaining Useful Life of a Turbofan Engine Using Deep Neural Network and Pearson Correlation Coefficient
Chang Woo Hong¹^{, *} ; Kyeon Hur²
1Lieutenant commander, Republic of Korea Navy Headquarter (ROKN HQ)/ Ph.D course student, Dept. of Electrical and Electronic Engineering, Yonsei University

2Professor, Dept. of Electrical and Electronic Engineering, Yonsei University

Correspondence to : ^*Chang Woo Hong Tel: +82-2-2123-7436 E-mail: spearw@yonsei.ac.kr
© 2021 Korea Society for Naval Science & Technology

초록

이 논문은 변수의 복잡성을 감소시키는 상관계수 분석을 사용하여 터보 팬 엔진의 잔여 유효수명(RUL)의 예측 정확도를 향상시키기 위한 심층 인공신경망 구조를 제안한다. 제안된 모델은 CNN(convolutional neural network), LSTM(long short-term memory) 및 양방향 LSTM(Bidirectional LSTM)을 적절히 배열하여 각 알고리즘의 성능을 최대화한다. 또한 제안된 모델의 학습 능력을 향상시키기 위해 residual network 및 dropout 기술을 적용한다. 제안된 방법의 성능은 기존 방법과의 비교 및 검증을 통해 낮은 상관 데이터를 제외한 제안된 방법의 유효성이 제시된다.

Abstract

This paper proposes a deep neural network architecture to improve the prognosis accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation coefficient analysis that reduces the variable complexity. The proposed model maximizes the performance of each algorithm by appropriately stacking convolutional neural network (CNN), long short-term memory (LSTM), and bidirectional LSTM. It also includes residual network and dropout technique to improve the learning performance of the proposed model. The performance of the proposed method is compared and verified with that of conventional method, which suggests the validity of the proposed method excluding the low correlated data.


Keywords: Deep Neural Network, Pearson Correlation Coefficient, Prognostics and Health Management, Turbofan Engine 키워드: 심층 신경망, 피어슨 상관계수, 건전성 예측관리, 터보팬 엔진

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