Remaining useful life prediction with insufficient degradation data based on deep learning approach

• Autorzy: Lyu Yi , Jiang Yijie , Zhang Qichen , Chen Ci

Identyfikatory

DOI

10.17531/ein.2021.4.17

• Języki publikacji: EN

Abstrakty

EN

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

Słowa kluczowe

EN

deep learning; remaining useful life; degradation data; data amplification; cycle-consistent generative adversarial network

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 4
• Strony: 745--756
• Opis fizyczny: Bibliogr. 37 poz., rys., tab

Twórcy

autor

Lyu Yi

• University of Electronic Science and Technology of China Zhongshan Institute, School of Computer, Zhongshan, China, 528400

autor

Jiang Yijie

• Guangdong University of Technology, Guangzhou, School of Automation, China, 510006

autor

Zhang Qichen

• University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu 611731

autor

Chen Ci

• Guangdong University of Technology, Guangzhou, School of Automation, China, 510006

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