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Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation
Yi Lyu 1,2
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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
Online publication date: 2023-04-28
Publication date: 2023-04-28
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(2):165811
  • A novel interval prediction model for RUL based on deep learning is proposed.
  • The interval prediction model combines the LSTM and LUBE to make full use of the timing information in the degradation data.
  • Experimental results show the RUL interval prediction performance has been significantly improved by the proposed method.
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 ShortTerm 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.
This research was funded by the Major Special Projects of Zhongshan (200824103628344) and the Guangdong Science and Technology Program (2021A0101180005).