Fault diagnosis methods based on deep learning and big data have achieved good results
on rotating machinery. However, the conventional deep learning method of bearing fault …

Accurate fault diagnosis of rolling bearing under variable working conditions can ensure that
the rotating machinery run in a safety, reliable and efficient way. In this paper, we propose …

For the established fault classification system, new faults cannot be identified due to lack of
training data in the process of equipment operation. Aiming at the problems of multi …

With the rapid development of industrial informatization and deep learning technology,
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …

A large amount of labeled data are important to enhance the performance of deep-learning-
based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality …

Effective fault diagnosis on rotating machinery is crucial for ensuring the reliability and safety
of mechanical equipment. However, available fault data are frequently scarce in real …

In recent years, intelligent fault diagnosis technology with deep learning algorithms has
been widely used in industry, and they have achieved gratifying results. Most of these …

In recent years, deep learning (DL) based intelligent fault diagnosis method has been widely
applied in the field of equipment fault diagnosis. However, most of the existing methods are …

Recent advances in data-driven methods have significantly promoted intelligent fault
diagnostics for varied industrial applications. However, due to the limitations of machine fault …

The label scarcity problem widely exists in industrial processes. In particular, samples of
some fault types are extremely rare; even worse, the samples of certain faults cannot be …