  Remaining useful life (RUL) estimation is a crucial component in the
implementation of intelligent predictive maintenance and health management.
Deep neural network (DNN) approaches have been proven effective in RUL
estimation due to their capacity in handling high-dimensional non-linear
degradation features. However, the applications of DNN in practice face two
challenges: (a) online update of lifetime information is often unavailable, and
(b) uncertainties in predicted values may not be analytically quantified. This
paper addresses these issues by developing a hybrid DNN-based prognostic
approach, where a Wiener-based-degradation model is enhanced with adaptive
drift to characterize the system degradation. An LSTM-CNN encoder-decoder is
developed to predict future degradation trajectories by jointly learning noise
coefficients as well as drift coefficients, and adaptive drift is updated via
Bayesian inference. A computationally efficient algorithm is proposed for the
calculation of RUL distributions. Numerical experiments are presented using
turbofan engines degradation data to demonstrate the superior accuracy of RUL
prediction of our proposed approach.
