Probabilistic remaining useful life prediction without lifetime labels: A Bayesian deep learning and stochastic process fusion method

Abstract

Trustworthy remaining useful life (RUL) predictions are critical for the long-term safe and reliable operation of degradation systems. The existing deep learning-based methods for RUL prediction are attracting increasing attention but typically face three main challenges. One is the absence of complete run-to-failure data, implying a lack of lifetime labels. Second, it is difficult to directly measure the health indicators (HIs) for field degradation systems. Third, the prediction models output point estimates without uncertainty. To this end, this paper proposes a Bayesian deep learning and stochastic process fusion method for probabilistic RUL prediction without lifetime labels. First, a model-free Bayesian neural network (BNN) is constructed to integrate the quantification of epistemic and aleatoric uncertainties in deep learning. Based on the constructed BNN, it is possible to predict the probability features of HIs. Then, degeneracy modeling is conducted using a nonlinear Wiener process to derive the probability density function of the RUL. Furthermore, model evolution can be achieved through parameter updating during online operations. Finally, the effectiveness and superiority of the proposed prediction method are verified on CALCE battery degradation data.