A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable
interest in the field of time series analysis, due to their ability to capture dependencies …
interest in the field of time series analysis, due to their ability to capture dependencies …
Multi-graph attention fusion graph neural network for remaining useful life prediction of rolling bearings
Y Xiao, L Cui, D Liu - Measurement Science and Technology, 2024 - iopscience.iop.org
Graph neural network (GNN) has the proven ability to learn feature representations from
graph data, and has been utilized for the tasks of predicting the machinery remaining useful …
graph data, and has been utilized for the tasks of predicting the machinery remaining useful …
Remaining life prediction of bearings based on improved IF-SCINet
J Zhang, C Zhang, S Xu, G Liu, H Fei, L Wu - IEEE Access, 2024 - ieeexplore.ieee.org
In the field of health management, predicting the remaining useful life (RUL) of a device
becomes critical. However, the RUL prediction process is often affected by a various of …
becomes critical. However, the RUL prediction process is often affected by a various of …
A novel data augmentation framework for remaining useful life estimation with dense convolutional regression network
Deep learning-based methods play an increasingly significant role in prognostic and health
management, enabling accurate and rapid estimation of the remaining useful life (RUL) …
management, enabling accurate and rapid estimation of the remaining useful life (RUL) …
A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health
Management (PHM), aimed at predicting the future state of a system to enable timely …
Management (PHM), aimed at predicting the future state of a system to enable timely …
Remaining useful life prediction of rotating equipment under multiple operating conditions via multi-source adversarial distillation domain adaptation
Recently, domain adaptation (DA) has been widely used in the remaining useful life (RUL)
prediction of rotating machinery to effectively mitigate domain shift. Traditional DA methods …
prediction of rotating machinery to effectively mitigate domain shift. Traditional DA methods …
A Multi-Task Learning Approach With Meta Auxiliary Generation Network for Remaining Useful Life Estimati
The Industrial Internet of Things (IIoT) has greatly facilitated prognostics and health
management of complex mechanical equipment by enabling seamless data collection from …
management of complex mechanical equipment by enabling seamless data collection from …
Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine
M Zhou, Y Li, Y Cao, X Ma, Z Xu - Reliability Engineering & System Safety, 2025 - Elsevier
Aircraft engines have complex structures and various operating conditions, which leads to
the health monitoring data of aircraft engines' high coupling in the spatial domain and time …
the health monitoring data of aircraft engines' high coupling in the spatial domain and time …
Multibranch Horizontal Augmentation Network for Continuous Remaining Useful Life Prediction
Aiming at the large differences between tasks in continuous remaining useful life (RUL)
prediction and the limited information capturing capability of the existing continuous learning …
prediction and the limited information capturing capability of the existing continuous learning …
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health
management of industrial systems that involve a variety of interrelated sensors. Given a …
management of industrial systems that involve a variety of interrelated sensors. Given a …