Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when. In this article, a novel deep neural network named convolution-based long short-term memory (CLSTM) network is proposed to predict the RUL of rotating machineries mining the in situ vibration data. Different from previous research that simply connects a convolutional neural network (CNN) to a long short-term memory (LSTM) network serially, the proposed network conducts convolutional operation on both the input-to-state and state-to-state transitions of the LSTM, which contains both…

• AF
  Air Force Office of Scientific Research

  Award: FA95501810491