Adaptive Degradation Process with Deep Learning-Driven Trajectory
This work addresses predictive maintenance for industrial systems like turbofan engines, but it is incremental as it builds on existing DNN and degradation models.
The paper tackled the challenges of online lifetime information updates and uncertainty quantification in remaining useful life (RUL) estimation by developing a hybrid deep learning approach with adaptive drift and Bayesian inference, achieving superior accuracy in numerical experiments on turbofan engine data.
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.