Bearing fault diagnosis has evolved from machine learning to deep learning, addressing the
issues of performance degradation in deep learning networks and the potential loss of key …

Focusing on the problems of complex structure and low feature extraction efficiency that
exist in some traditional neural network algorithms, an improved convolutional neural …

The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …

Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of
manufacturing equipment. In the actual complex environment, the collected bearing signals …

Real-time monitoring and fault diagnosis of bearings are of great significance to improve
production safety, prevent major accidents, and reduce production costs. However, there are …

Convolutional neural networks (CNNs) serve as powerful feature extraction tools capable of
effectively extracting information from complex environments, thus improving the accuracy of …

To solve the problems that existing bearing fault diagnosis methods cannot adaptively select
features and are difficult to deal with noise interference, an end-to-end fault diagnosis …

Fault diagnosis of rolling bearings is significant for mechanical equipment operation and
maintenance. Presently, the deep convolutional neural network (CNN) is increasingly used …

Feature extraction from a time sequence signal without manual information is an important
part for bearing intelligent diagnosis. With the merits of signal information and feature …

In recent years, deep learning has achieved great success in bearing fault diagnosis due to
its robust feature learning capabilities. However, in the actual industry, the diagnostic …