Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …

Recently, deep learning techniques have significantly bolstered the advancement of
intelligent fault diagnosis. However, in engineering practice, the limited availability of fault …

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …

Intelligent fault diagnosis of rolling bearing issues have been well addressed with the rapid
growth of data scale. However, the performance of most diagnostic algorithms heavily …

In the recent years, deep learning-based intelligent fault diagnosis methods of rolling
bearings have been widely and successfully developed. However, the data-driven method …

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 …

At present, the success of most intelligent fault diagnosis methods is heavily dependent on
large datasets of artificial simulation faults (ASF), which have not been widely used in …

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary
mechanical system. In practice, the sample proportion between faulty data and healthy data …

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized
in the fault diagnosis field. However, there are two common problems in deep-learning …

Different fault types of rolling bearings correspond to different features, and classical deep
learning models using a single attention mechanism (AM) have limitations in capturing …