Adaptive Degradation Process with Deep Learning-Driven Trajectory

TL;DR

This paper introduces a hybrid deep learning approach that combines a Wiener-based degradation model with adaptive drift updating via Bayesian inference, improving RUL prediction accuracy in predictive maintenance.

Contribution

It develops a novel LSTM-CNN encoder-decoder model with adaptive drift for more accurate RUL estimation, addressing practical challenges of online updates and uncertainty quantification.

Findings

Superior RUL prediction accuracy demonstrated on turbofan engine data

Efficient algorithm for RUL distribution calculation

Effective adaptive drift updating via Bayesian inference

Abstract

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.