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 …

Data-driven deep learning methods have been widely used in the fault diagnosis of rolling
bearings, while general network structures are complex with numerous parameters and …

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 …

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 …

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 …

As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has
been a research hotspot. In order to diagnose the faults of rolling bearing under different …

Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in
time and reduce losses, this paper presents an intelligent diagnosis method for rolling …

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

Considering the nonstationary characteristics of the vibration signal of aircraft engine rolling
bearings and the insufficient ability of convolutional neural network (CNN) to extract …

Traditional methods of rolling bearing fault diagnosis generally have the following
disadvantages: low accuracy of fault severity identification, the need for artificial feature …