Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …

Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …

Transfer learning based on a single source domain to a target domain has received a lot of
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …

The traditional on-house sensing (OHS) accelerometer for vibration measurements causes
poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the …

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 …

In the past few years, deep learning (DL) techniques for predicting remaining useful life
(RUL) have shown remarkable advancements, but model prediction accuracy and …

Existing researches about unsupervised cross-domain bearing fault diagnosis mostly
consider global alignment of feature distributions in various domains, and focus on relatively …

Helical gearboxes play a critical role in power transmission of industrial applications. They
are vulnerable to various faults due to long-term and heavy-duty operating conditions. To …

Recent research in semi-supervised fault diagnosis of machinery based on graph neural
networks (GNNs) still has some problems, such as insufficient label information mining …