The collected vibration data with labeled information from bearing is far insufficient in
engineering practice, which is challenging for training an intelligent diagnosis model. For …

Transfer learning exhibits exciting advantages in solving the data shortage in fault
diagnosis, while most of the existing methods still need target domain fault data, which …

The research of intelligent fault diagnosis method has made great progress. The
prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant …

Fault diagnosis is an important technology in the development of modern industrial safety.
Vibration information is commonly used to determine the state of bearings. Driven by big …

The effective fault diagnosis of rolling bearings is of great importance in guaranteeing the
normal operation of rotating machinery. However, measured rolling bearing vibration signals …

Automatic and accurate identification of rolling bearings fault categories, especially for the
fault severities and fault orientations, is still a major challenge in rotating machinery fault …

Rolling bearings are the vital components of rotary machines. The collected data of rolling
bearing have strong noise interference, massive unlabeled samples, and different fault …

Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery.
Existing methods use signals from a single dimension, limiting diagnostic generality and …

A multi-ensemble method is proposed based on deep auto-encoder (DAE) for fault
diagnosis of rolling bearings. At first, several DAEs with different activation functions are …

Mechanical intelligent fault diagnosis is an important method to accurately identify the health
status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the …