Deep learning-based remaining useful life (RUL) prediction methods have achieved great
success due to their powerful capacity of feature representation especially when big data of …

Remaining useful life (RUL) predictions are a key enabler for predictive maintenance. Data-
driven approaches, typically based on deep neural networks (DNNs), have shown success …

Early detection of faulty patterns and timely scheduling of maintenance events can minimize
risk to the underlying processes and increase a system's lifespan, reliability, and availability …

Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in
enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial …

Unsupervised domain adaptation (UDA) strives to transfer the learned knowledge to
differently distributed datasets using both source and target data. Recently, an increasing …

The prediction of the remaining useful life (RUL) is of great significance to ensure the safe
operation of industrial equipment and to reduce the cost of regular preventive maintenance …

Aeroengines are the core components of an aircraft; therefore, their health determines flight
safety. Currently, owing to their complex structure and problems associated with their …

Traditional batteries or external supply powered wireless sensing system are needed to be
improved for realizing the development of the smart industry with low-carbon, green and …

Remaining useful life (RUL) prediction of components is a crucial aspect of mechanical
systems prognostic and health management. However, during the online RUL prediction of …

Remaining useful life (RUL) prediction is of great significance to ensure the safety and
reliability of equipment. Graph neural network (GNN)-based methods show great potential to …