Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …

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 …

Existing studies on meta-learning based few-shot fault diagnosis largely focus on constant
speed scenarios, neglecting the consideration of more realistic scenarios involving unstable …

As a key component widely used in electric multiple units (EMU), fault diagnosis of EMU
bearing is an important link. Typically, labeled data from different conditions provides the …

Compound fault diagnosis of bearings has always been a challenge, due to the occurrence
of various faults with randomness and complexity. Existing deep learning-based methods …

The accurate diagnosis of the compound fault of industrial robots can be highly beneficial to
maintenance management. In the actual noisy working environment of industrial robots, the …

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 …

Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

Due to the growing interest for increasing productivity and cost reduction in industrial
environment, new techniques for monitoring rotating machinery are emerging. Artificial …