Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

When the machinery device operates abnormally, it is not sufficient for fault detection only
via extracting fault features from a single sensor due to the latent fault information may be …

Federated learning is an effective technique to solve the problem of information fusion and
information sharing in intelligent vehicle networking. However, most of the existing federated …

Due to the extremely limited prior knowledge, machinery fault diagnosis under varying
working conditions with limited annotation data is a very challenging task in practical …

The fault diagnosis of rolling bearings is of utmost importance in industrial applications to
ensure mechanical systems' reliability, safety, and economic viability. However …

Collaborative fault diagnosis has become a hot research topic in fault detection and
identification, greatly benefiting from emerging multisensory fusion techniques and newly …

In recent years, the rapid development of convolutional neural networks (CNNs) has
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …

Rolling bearings are the core components of rotating machinery, and their normal operation
is crucial to the entire industrial production. Most existing condition monitoring methods have …

Deep Learning has been attracting considerable attention as it can autonomously learn
important signal features and has shown great potential for fault diagnosis. However, given …

Deep learning (DL) has attained remarkable achievements in diagnosing faults for rotary
machineries. Capitalizing on the formidable learning capacity of DL, it has the potential to …