Deep learning for prognostics and health management: State of the art, challenges, and opportunities

•The state-of-the-art deep models in PHM applications have been overviewed.•The models are classified into generative, discriminative and hybrid categories.•The transfer learning and domain adaptation in the context of PHM are discussed.•Important challenges and future research directions have been...

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Bibliographic Details
Published in:	Measurement : journal of the International Measurement Confederation Vol. 163; p. 107929
Main Authors:	Rezaeianjouybari, Behnoush, Shang, Yi
Format:	Journal Article
Language:	English
Published:	London Elsevier Ltd 15.10.2020 Elsevier Science Ltd
Subjects:	Acoustic emission Acoustics Aerospace engineering Anomalies Anomaly detection Artificial neural networks Condition monitoring Declination Deep learning Domain adaptation Fault diagnosis Machine learning Neural networks Nuclear energy Nuclear engineering Nuclear reactors Prognostics and health management Reliability engineering Signal monitoring State-of-the-art reviews Systems health monitoring Vibration Vibration monitoring HI NAS CBLSTM DNN GDA CPS AdaBN DL MCMC CPU GPU DBM Deep learning MLP DBN WPI RNN SDAE FFT RBF EMA JSD WPT RBM SSAE AE AAE MMD CAE SaaS AI Prognostics and health management AFSA PSO Anomaly detection PSR RKH SSDAE SML GAN SAE STPN WJDA RL MSCNN CNN CORAL PaaS BLSTM IIoT CVAE GDBM KNN GRU SVM Fault diagnosis CLSTM GRU-ED PHM HHT WGAN AHKL SNR Domain adaptation TPU DQN ACGAN CD KL LSTM TDConvLSTM DAD IaaS SPEV DAE SDAE-NCL SCDA CUDA SGD CDBN LSTM-ED TL VAE DA
ISSN:	0263-2241, 1873-412X
Online Access:	Get full text
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