The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …

A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …

Deep learning-based bearing fault diagnosis has been systematically studied in recent
years. However, the success of most of these methods relies heavily on massive labeled …