RUL prediction by LSTM model with Bayesian parameter optimization for turbine engines

Abstract

Effectively predicting the remaining useful life (RUL) of a product is significant for reasonable reliability planning and maintenance activities. The long-short term memory (LSTM) model, which belongs to deep learning methods, was applied for RUL prediction of turbine engines, and a parameter optimization method with Bayesian theory was studied.