The scarcity of fault samples has been the bottleneck for the large-scale application of
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …

Recently, deep learning-based intelligent fault diagnosis methods have been developed
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …

Deep learning-based methods have been developed and widely used for fault diagnosis,
which rely on the sufficient data. However, fault data are extremely limited in some real-case …

Considering the changing working conditions of rotating machinery in operation, it is often
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …

Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …

In the engineering practice, lacking of data especially labeled data typically hinders the wide
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …

In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous
development thanks to its powerful feature representation ability. However, scarcity of high …

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 …

In this article, we study the challenging few-shot fault diagnosis (FSFD) problem where
limited faulty samples are available. Metric-based meta-learning methods have been a …

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent
manufacturing systems. Deep learning-based methods have been recently developed for …