Most data-based bearing fault intelligent diagnosis methods have assumed that all data is
under the same working conditions. However, the fault data under unknown working …

Bearing fault diagnosis plays an important role in rotating machinery equipment's safe and
stable operation. However, the fault sample collected from the equipment is seriously …

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 …

Due to the concurrency and coupling of various types of faults, and the number of possible
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …

Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to
severe data distribution difference under variable working conditions, they have …

Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing
to the different fault parameters coupling, fault characteristics diversity, and the exponential …

Compound fault identification has always been a challenge in bearing fault diagnosis.
Existing learning-based compound fault diagnosis methods require numerous labeled …

Most current intelligent fault diagnosis models, dependent on specific working condition data
for training, cannot effectively diagnose faults in unknown working conditions without data …

Deep learning-based bearing fault diagnosis methods have been developed to learn fault
knowledge from massive data. Owing to the deficiency of fault samples and the variability of …

Rolling bearings are susceptible to failure because of their complex and severe working
environments. Deep learning-driven intelligent fault diagnosis methods have been widely …