Deep learning method has obtained abundant achievements in remaining useful life (RUL)
prediction, which can steer the preventive maintenance decision-making for improving 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 …

Machinery remaining useful life (RUL) prediction plays a pivotal role in modern industrial
maintenance. Traditional methods entail the manual selection of useful features, which …

Accurate monitoring of mechanical device conditions requires a large number of sensors
working together. There are potential connections between sensors throughout the …

In recent years, prognostic and health management (PHM) has played an important role in
industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the …

Accurate estimating the machine health indicator is an essential part of industrial
intelligence. Despite having considerable progress, remaining useful life (RUL) prediction …

Predictive maintenance (PdM) is useful for engineers to schedule maintenance flexibly, to
operate equipment efficiently, and also to avoid unexpected downtime. Remaining useful life …

The prediction of remaining useful life (RUL) has attracted much attention, and it is also a
key section for predictive maintenance. In this study, a novel hybrid deep learning framework …

For prognostics and health management of mechanical systems, a core task is to predict the
machine remaining useful life (RUL). Currently, deep structures with automatic feature …

This paper proposes a multi-head neural network (MHNN) model with unsymmetrical
constraints for remaining useful life (RUL) prediction of industrial equipment. Generally, the …