Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges
V Nasir, F Sassani - The International Journal of Advanced Manufacturing …, 2021 - Springer
Data-driven methods provided smart manufacturing with unprecedented opportunities to
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine
Remaining useful life (RUL) prediction is the core research task of aero-engine prognostics
health management (PHM), which is crucial to promoting the safety, reliability and economy …
health management (PHM), which is crucial to promoting the safety, reliability and economy …
A prognostic driven predictive maintenance framework based on Bayesian deep learning
Recent years have witnessed prominent advances in predictive maintenance (PdM) for
complex industrial systems. However, the existing PdM literature predominately separates …
complex industrial systems. However, the existing PdM literature predominately separates …
Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
Prediction of remaining useful life (RUL) is of vital significance in the prognostics health
management (PHM) tasks. To deal with the reverse time series and to reflect the difference …
management (PHM) tasks. To deal with the reverse time series and to reflect the difference …
An integrated multi-head dual sparse self-attention network for remaining useful life prediction
Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial
role in prognostics health management technology. Conventional convolutional neural …
role in prognostics health management technology. Conventional convolutional neural …
A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings
Remaining useful life (RUL) prediction has been a hotspot in the engineering field, which is
useful to avoid unexpected breakdowns and reduce maintenance costs of the system. Due …
useful to avoid unexpected breakdowns and reduce maintenance costs of the system. Due …
Physics-Informed LSTM hyperparameters selection for gearbox fault detection
A situation often encountered in the condition monitoring (CM) and health management of
gearboxes is that a large volume of CM data (eg, vibration signal) collected from a healthy …
gearboxes is that a large volume of CM data (eg, vibration signal) collected from a healthy …
A parallel hybrid neural network with integration of spatial and temporal features for remaining useful life prediction in prognostics
Prediction of remaining useful life (RUL) is an indispensable part of prognostics health
management (PHM) in complex systems. Considering the parallel integration of the spatial …
management (PHM) in complex systems. Considering the parallel integration of the spatial …
Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit
Abstract Remaining Useful Life (RUL) estimation is a fundamental task in the prognostic and
health management (PHM) of industrial equipment and systems. To this end, we propose a …
health management (PHM) of industrial equipment and systems. To this end, we propose a …