bidirectional handshaking lstm for remaining useful life prediction文献.pdf

Neurocomputing 323 (2019) 148–156
Contents lists available at Sc Memory (LSTM) neural network models have been demonstrated to be efficient throughout the literature
Keywords:

Remaining useful life prediction when dealing with sequential data because of their ability to retain a lot of information over time about

Bidirectional handshaking previous states of the system. This paper proposes using a new LSTM architecture for predicting the RUL
Long Short-Term Memory when given short sequences of monitored observations with random initial wear. By using LSTM, this pa-
Asymmetric objective function per proposes a new objective function that is suitable for the RUL estimation problem, as well as a new
Target generation target generation approach for training LSTM networks, which requires making lesser assumptions about
the actual degradation of the system.