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 …

Edge computing is an emerging paradigm that offloads the computations and analytics
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …

Deep belief network (DBN) is one of the most representative deep learning models.
However, it has a disadvantage that the network structure and parameters are basically …

Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …

In the mechanical transmission system, the gear is one of the most widely used transmission
components. The failure of the gear will cause serious accidents and huge economic loss …

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …

Traditional feature extraction methods are used to extract the features of signal to construct
the fault feature matrix, which exists the complex structure, higher correlation, and …

In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …

Recent works suggest that using knowledge transfer strategies to tackle cross-domain
diagnosis problems is promising for achieving engineering diagnosis. This article presents a …

Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …