Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …

Rapidly evolving renewable energy generation technologies and the ever-increasing scale
of renewable energy installations are driving the need for more accurate, faster, and smarter …

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of
engineering equipment. Many intelligent diagnosis methods have been successfully …

Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …

It is significant to boost the performance of fault diagnosis of wind turbine gearboxes. In this
paper, a hybrid attention improved residual network (HA-ResNet) based method is proposed …

Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …

Vibration signals under the same health state often have large differences due to changes in
operating conditions. Likewise, the differences among vibration signals under different …

Effective fault diagnosis of rotating machinery is essential for the predictive maintenance of
modern industries. In this study, a novel framework that combines a residual network …

In industrial systems, the vibration signals of rolling bearings are influenced by changing
operating conditions and strong environmental noise, therefore they are often characterized …