Effective detection of fault in rolling bearings with a limited amount of data is essential for the
safe operation of electric machines. This article proposes a novel meta-learning-enabled …

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 …

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 …

Deep learning for bearing fault diagnosis often requires a large quantity of comprehensive
data to give support in the field of rotating machinery fault diagnosis. However, large …

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 …

The rapid development of artificial intelligence and deep learning has provided many
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …

Most fault identification methods based on deep learning rely on a large amount of data, and
their effects are limited in the actual production environment. In the case of multiple …

Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …

The real-world large industry has gradually become a data-rich environment with the
development of information and sensor technology, making the technology of data-driven …

Deep learning models have been widely studied in fault diagnosis recently. A mainstream
application is to recognize patterns in spectrograms of faults. However, some common …