Abstract
In modern industrial systems, sensor data reflecting the system health state are commonly used for the remaining useful lifetime (RUL) prediction, which are increasingly processed by modern deep learning based approaches recently. But these deep learning models do not automatically provide uncertainty information for the RUL prediction, hence this paper is motivated to introduce a novel approach that allows to control trade-off between prediction performance and knowledge about the uncertainty of the RUL prediction. The key aspect of our approach is to use a long short-term memory (LSTM) network as an expressive black-box predictor and the Wiener process as a surrogate to model the propagation of prediction uncertainty. The uncertainty propagation model is used to interactively train the RUL predictor. Our empirical results in a turbofan engine degradation simulation use case show that the surrogate Wiener propagation model can improve the near-failure prediction accuracy by sacrificing the far-to-failure prediction accuracy.
Original language | English |
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Article number | 106727 |
Number of pages | 10 |
Journal | Reliability Engineering and System Safety |
Volume | 196 |
DOIs | |
Publication status | Published - Apr 2020 |
Funding
This work was supported by European Union’s Horizon 2020 grant no. 766994 (PROPHESY). Y. Deng also thanks for the support from National Natural Science Foundation of China (NSFC) grant no. 71701143 .
Funders | Funder number |
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European Union's Horizon 2020 - Research and Innovation Framework Programme | |
European Union's Horizon 2020 - Research and Innovation Framework Programme | |
European Commission | 766994 |
National Natural Science Foundation of China | 71701143 |
Keywords
- Long short-term memory
- Recurrent neural network
- Remaining useful lifetime
- Surrogate modeling
- Uncertainty propagation
- Wiener process