Deep learning has achieved numerous breakthroughs in bearing predicting remaining
useful life (RUL). However, the current mainstream deep learning framework inevitably has …

Rolling bearings, an essential fundamental component in machinery and equipment, have
been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps …

Deep Learning has been attracting considerable attention as it can autonomously learn
important signal features and has shown great potential for fault diagnosis. However, given …

The prediction of the remaining useful life (RUL) of bearings is of great significance for
reducing cost and increasing efficiency of mechanical equipment and ensuring healthy …

In industry, accurate remaining useful life (RUL) prediction is critical in improving system
reliability and reducing downtime and accident risk. Numerous data-driven RUL prediction …

Accurate remaining useful life (RUL) prediction of rolling bearings plays a vital role in
ensuring the safe operation of mechanical equipment. Graph-based models have become …

The article presents a framework for fault detection and classification to monitor the condition
of motor bearings under multiple operating conditions. The condition monitoring of motor …

Remaining useful life (RUL) is an important index indicating the health status of equipment,
which has attracted extensive attention. Nevertheless, many existing RUL prediction …

Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction
of maintenance costs for turbofan engine systems. However, most of the existing methods …

In addressing the problem of low prediction accuracy in the remaining useful life (RUL)
prediction of rolling bearings, caused by noise interference and insufficient extraction of …