Using temporal convolution network for remaining useful lifetime prediction

Using temporal convolution network for remaining useful lifetime prediction

Abstract Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short‐Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long...

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Bibliographic Details
Main Authors: Jungan Chen, Danjiang Chen, Gaoping Liu
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:Engineering Reports
Subjects:
CNN
deep learning
LSTM
remaining useful lifetime prediction
Online Access:https://doi.org/10.1002/eng2.12305

Internet

https://doi.org/10.1002/eng2.12305

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