In recent decades, fault diagnosis methods based on machine learning and deep learning
have achieved excellent results in fault diagnosis and are characterized by powerful …

Benefiting from the excellent capability of data processing, deep learning-based methods
have been well applied in fault diagnosis. However, these methods may perform poorly due …

Recently, the progress of intelligent fault diagnosis shows deep learning-based methods
with large data have achieved great success. Nevertheless, in engineering practice, limited …

Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled
training samples. However, in real industry applications, labeled data are scarce or even …

The techniques of machine learning, as well as deep learning (DL) methods, have seen a
wide application in the intelligent fault diagnosis field these years. However, contemporary …

The success of deep learning is based on a large number of tagged data, which is
challenging to satisfy on many occasions. Especially in industry fault diagnosis, considering …

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 …

In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based
on traditional machine learning and deep learning, have been developed and successfully …

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 practical industrial applications, it is crucial to train a robust fault diagnosis (FD) model that
can quickly adapt to new working conditions or fault modes using a few labeled fault …