Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …

Due to the extremely limited prior knowledge, machinery fault diagnosis under varying
working conditions with limited annotation data is a very challenging task in practical …

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

While transfer learning-based intelligent diagnosis has achieved significant breakthroughs,
the performance of existing well-known methods still needs urgent improvement, given the …

Owing to the harsh operating environment of rolling bearings, acquired vibration signals
contain strong noise interference, which makes it challenging for conventional methods to …

A precision multi-sensor monitoring strategy is required to meet the challenges posed by
increasingly complex products and manufacturing processes during laser welding. In this …

Rotating machinery plays an essential part in many engineering fields. It needs prompt
solutions to the prognosis and health management to ensure the system reliability …

Current rotating machinery fault diagnosis methods are primarily based on deep learning
methods, which necessitate a large amount of data for training to achieve better results …

Cross-domain fault diagnosis based on transfer learning has been popularly developed to
overcome inconsistent data distribution-caused degradation of diagnostic performance …

The current deep learning-based intelligent diagnosis algorithms depend on large amounts
of well-labeled data, but they may not perform well in engineering practice where the fault …