Remaining Life Prediction for Aircraft Turbine Engines Based on LSTM-RNN - HMM - APPROACH

Abstract

Prognostics and Health Management (PHM) of the aircraft gas turbine engine is essential in the safety of the aircraft. In this paper, engine remaining useful life (RUL) was predicted with a novel architecture based on a hybrid recurrent neural network. This hybrid model trains HMM firstly and then gives a small LSTM to get distributions of HMM states. These HMM states are further trained to fill in gaps in HMM. Subsequently, a jointly trained hybrid model is constructed, which can enhance stability and accuracy of prediction significantly.