A Hybrid Bayesian Deep Learning Model for Remaining Useful Life Prognostics and Uncertainty Quantification

Remaining useful life (RUL) prognostics is critical to maintenance decision making and reliability assessment. A significant amount of neural networks research has been reported to develop prognostics models that can provide high prediction accuracy. These models use advanced neural networks to improve prediction performance and provide sole point estimation for RUL. However, accurate uncertainty quantification of the RUL is essential to understand the uncertainty of the degradation process and perform reliable risk analysis and maintenance decisions. This paper proposes a new hybrid Bayesian deep learning (HBDL)-based prognostics approach. It uses long short term memory autoencoder (LSTM-AE) to extract features that include degradation information, and then uses the Bayesian neural network (BNN) to model and predict the equipment degeneration process. It learns network weights through variational inference (VI) and provides RUL prognostics results while obtaining interval estimation. Finally, a general aircraft engine data set is used to verify the proposed model. The experimental results show that this method can achieve satisfying prediction accuracy and uncertainty quantification capability.