With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited
samples is challenging in industrial practice. The existing limited samples methods usually …

Unsupervised cross-domain fault diagnosis of bearings has practical significance; however,
the existing studies still face some problems. For example, transfer diagnosis scenarios are …

As one of the representative unsupervised data augmentation methods, generative
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …

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

As the connecting hub of the airport runways and gates, the taxiway plays a very important
role in the rational allocation and utilization of the airport resources. In this paper, a multi …

The existing fault diagnosis methods of rotating machinery constructed with both shallow
learning and deep learning models are mostly based on vibration analysis under steady …

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things
(IoMT) systems with multiple technologies for digital healthcare applications has been …

Early subsurface cracks make significant changes to the dynamic responses of full ceramic
ball bearings, and complex space environment brings challenges to the status monitoring of …