In fault diagnosis, machine learning theories are gaining popularity as they proved to be an
efficient tool that not only reduces human effort but also identifies the health conditions of the …

Cross-domain fault diagnosis based on transfer learning has been popularly developed to
overcome inconsistent data distribution-caused degradation of diagnostic performance …

As one of the key components of the train, the condition of the bearing is related to the train's
safe operation. The vibration signal of the bearing is usually nonlinear and nonstationary …

Recently, deep learning has been widely used for intelligent fault diagnosis of rolling
bearings due to its no-mankind feature extraction capability. The majority of intelligent …

Large electromechanical equipment typically operates under low-speed, heavy-duty
conditions, significantly increasing the likelihood of bearing failures. The reduced speed …

Purpose: This paper aims to enhance bearing fault diagnosis in industrial machinery by
introducing a novel method that combines Graph Attention Network (GAT) and Long Short …

Bearing fault diagnosis is vital for saving invaluable time and cost since it is the most critical
components in rotary machines. The feature fusion method has been a effective way to …

Bearing fault diagnosis is vital to safeguard the heath of rotating machinery. It can help to
avoid economic losses and safe accidents in time. Effective feature extraction is the premise …

Fault diagnosis of bearing in mechanical equipment is critical for ensuring safety and saving
costs. The feature fusion technology is a effective way to improve the performance of fault …

Aiming at the problem of noise interference leading to poor fault diagnosis effect of rolling
bearing, a two-stage signal noise reduction method based on multi-strategy coati …