Predicting the remaining useful life (RUL) of rolling bearings can effectively prevent the
breakdown of rotating machinery systems and catastrophic accidents. Most existing RUL …

Effective feature extraction is crucial for accurate fault diagnosis of rolling bearings. A novel
feature extraction method called hierarchical dispersion entropy (HDE) based on …

Various working conditions and bearing structures make remaining useful life (RUL)
prediction more challenging. This article presents a new intermediate-domain support vector …

Predicting the remaining useful life (RUL) of rolling bearings under different working
conditions improved significantly by transfer learning. However, existing methods have not …

Convolution neural network (CNN) has been widely used in the field of remaining useful life
(RUL) prediction. However, the CNN-based RUL prediction methods have some limitations …

Poor model generalization, missing or false alarms, and heavy dependence on expert's
experience are some of the major problems which exist in traditional incipient fault detection …

Degradation indicator (DI) can represent the degradation trend and status of machines,
providing decision support in the operational maintenance of machines. However, the …

The prediction of the remaining useful life (RUL) of bearings could reduce maintenance
costs and prevent serious consequences. Due to the variable working conditions of …

Metal-oxide-semiconductor field-effect transistors (MOSFETs) are the core components of
electronic devices. Implementing effective and accurate remaining useful life (RUL) …

This article addresses the problem that the remaining useful life (RUL) prediction accuracy
for a high-speed rail catenary is not accurate enough, leading to costly and time-consuming …