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[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
A systematic literature review on transfer learning for predictive maintenance in industry 4.0
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and
digital technologies within industrial production and manufacturing systems. The objective of …
digital technologies within industrial production and manufacturing systems. The objective of …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …
A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction
Remaining useful life (RUL) prediction plays a significant role in the prognostic and health
management (PHM) of rotating machineries. A good health indicator (HI) can ensure the …
management (PHM) of rotating machineries. A good health indicator (HI) can ensure the …
Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data
Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …
and testing datasets are from identical machines operating under similar conditions …
Bi-LSTM-based two-stream network for machine remaining useful life prediction
In industry, prognostics and health management (PHM) is used to improve the system
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
Domain adaptive remaining useful life prediction with transformer
Prognostic health management (PHM) has become a crucial part in building highly
automated systems, whose primary task is to precisely predict the remaining useful life …
automated systems, whose primary task is to precisely predict the remaining useful life …
Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions
Since classical deep learning (DL) techniques are hungry for massive data and suffer from
domain shift, domain adaptation (DA) methods are broadly adopted in prognostics and …
domain shift, domain adaptation (DA) methods are broadly adopted in prognostics and …
Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
Dynamic model-assisted bearing remaining useful life prediction using the cross-domain transformer network
Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to
various industrial applications. Recently, intelligent data-driven RUL prediction methods …
various industrial applications. Recently, intelligent data-driven RUL prediction methods …