Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

This paper proposes a novel hybrid method aiming at the fault prognosis of bearings. A
nonlinear health indicator (HI) is first constructed using Complete Ensemble Empirical Mode …

Accurate prediction of remaining useful life (RUL) is necessary to ensure stable and safe
operations for rocket engines. The paper proposed a multi-head attention network coupled …

The precise maintenance and scientific management of large and complex mechanical
equipment are of great significance for ensuring the safe operation of equipment and …

Two-stage prediction methods based on Wiener processes are widely used to describe the
degradation process of components. However, a single type of drift function cannot …

Based on the multistage and nonlinear characteristics of cutting tool wear, a hybrid method
for cutting tool remaining useful life (RUL) prediction based on convolutional neural network …

Data-driven remaining useful life (RUL) prediction approaches, especially those based on
deep learning (DL), have been increasingly applied to mechanical equipment. However, two …

Proton exchange membrane fuel cells offer a sustainable solution to electrical power
generation and combined power and heat applications, even if they still encounter durability …

When the field collected data is biased by unexpected errors due to sensors and
measurement, simple Wiener process may fail to correctly estimate the true degradation …

The accurate estimation of remaining useful life (RUL) is significant for the operation,
maintenance, and avoidance of unplanned downtime of rotating machinery. To improve the …