Appropriate maintenance of industrial equipment keeps production systems in good health
and ensures the stability of production processes. In specific production sectors, such as the …

Data-driven methods provided smart manufacturing with unprecedented opportunities to
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …

Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial
role in prognostics health management technology. Conventional convolutional neural …

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 …

The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …

Remaining useful life (RUL) estimation has been intensively studied, given its important role
in prognostics and health management (PHM) of industry. Recently, data-driven structures …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Remaining useful life (RUL) prediction is one of the key technologies of condition-based
maintenance (CBM), which is important to maintain the reliability and safety of industrial …

In the Engineering discipline, prognostics play an essential role in improving system safety,
reliability and enabling predictive maintenance decision-making. Due to the adoption of …

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 …