Open AccessRemaining Life Prediction for Aircraft Turbine Engines Based on LSTM-RNN - HMM – APPROACH
Author(s) -
Jing Bi,
Wen-Ze Fan,
Shoubin Wang
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/2/022033
Subject(s) - prognostics , hidden markov model , train , turbine , computer science , recurrent neural network , artificial neural network , gas turbines , aero engine , engineering , artificial intelligence , data mining , aerospace engineering , mechanical engineering , cartography , geography
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.