A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develops a novel prognostic method based on bidirectional long short-term memory (BLSTM) networks. The method can integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. The proposed architecture based on BLSTM networks includes three main parts: first, one BLSTM network is used to directly extract features hidden in the multiple raw sensors signals; second, another BLSTM…

• NN
  National Natural Science Foundation of China

  Award: 51405065
• CS
  China Scholarship Council

  Award: 201706070061