Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation

• Autorzy: Lyu Yi , Zhang Qichen , Chen Aiguo , Wen Zhenfei

Identyfikatory

DOI

10.17531/ein/165811

• Języki publikacji: EN

Abstrakty

EN

Deep learning is widely used in remaining useful life (RUL) prediction because it does not require prior knowledge and has strong nonlinear fitting ability. However, most of the existing prediction methods are point prediction. In practical engineering applications, confidence interval of RUL prediction is more important for maintenance strategies. This paper proposes an interval prediction model based on Long Short-Term Memory (LSTM) and lower upper bound estimation (LUBE) for RUL prediction. First, convolutional auto-encode network is used to encode the multi-dimensional sensor data into one-dimensional features, which can well represent the main degradation trend. Then, the features are input into the prediction framework composed of LSTM and LUBE for RUL interval prediction, which effectively solves the defect that the traditional LUBE network cannot analyze the internal time dependence of time series. In the experiment section, a case study is conducted using the turbofan engine data set CMAPSS, and the advantage is validated by carrying out a comparison with other methods.

Słowa kluczowe

EN

remaining useful life; lower upper bound estimation; Long Short-Term Memory; prediction interval

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 2
• Strony: art. no. 165811
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Lyu Yi

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

• https://orcid.org/0000-0001-5150-3482

autor

Zhang Qichen

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

• https://orcid.org/0009-0005-3906-5172

autor

Chen Aiguo

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

• https://orcid.org/0000-0002-3712-2349

autor

Wen Zhenfei

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

• https://orcid.org/0009-0000-6304-720X

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