The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …

Abstract Domain adaptation-based fault diagnosis (DAFD) methods have been explored to
address cross-domain fault diagnosis problems, where distribution discrepancy exists …

Deep neural networks have been successfully utilized in the mechanical fault diagnosis,
however, a large number of them have been based on the same assumption that training …

In recent years, many researchers have attempted to achieve cross-domain diagnosis of
faults through domain adaptation (DA) methods. However, owing to the complex physical …

Despite the recent success of deep-learning-based fault diagnosis of rotating machinery, to
enable accurate and robust diagnosis models, existing approaches proceed with the …

Recently, domain generalization techniques have been introduced to enhance the
generalization capacity of fault diagnostic models under unknown working conditions. Most …

Intelligent fault diagnosis technology, as a promising approach, is gradually playing an
irreplaceable role in ensuring the safety, reliability, and efficiency of mechanical equipment …

Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …