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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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
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Summary:	•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 provided. Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0263-2241 1873-412X
DOI:	10.1016/j.measurement.2020.107929