Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model. (April 2020) - Deng, Yingjun; Bucchianico, Alessandro Di; Pechenizkiy, Mykola
Highlights: A new scope of sequential RUL prediction compared with conventional point-wise prediction. A surrogate modeling framework for uncertainty propagation using drifted Wiener processes. Hybrid modeling between machine learning and statistics Improve the near-failure prediction accuracy by sacrificing the far-to-failure prediction accuracy. 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.