Abstract Predictive Maintenance (PdM) has continually attracted interest from the
manufacturing community due to its significant potential in reducing unexpected machine …

Recently, rotating machinery fault diagnosis studies based on graph neural networks (GNN)
have received some satisfactory achievements. But most of them are based on the analysis …

Vibration, current, and acoustic signals have different advantages and characteristics in fault
diagnosis. Although a few researches have explored their fusion methods and applied them …

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 …

The proposed intelligent framework seamlessly integrates degradation monitoring, defect
identification, and remaining useful life (RUL) estimation for a comprehensive and holistic …

Nowadays, the health management of rotating machinery based on deep learning has
achieved remarkable results. Nevertheless, in the presence of variable working conditions …

Leveraging generalized knowledge from multiple source domains with rich labels to the
target domain without labeled data is a more realistic and challenging issue compared with …

Intelligent fault diagnosis has made significant progress, thanks to machine learning,
particularly deep-learning algorithms. However, most machine-learning algorithms treat …

The critical issue of wheeled robot fault diagnosis is to comprehensively evaluate its health
condition using multisensor data, but traditional deep learning-based methods are hard to …

Massive amounts of industrial data, which are often gathered by industrial control systems
(ICS), have been generated by the fast growth of industrial intelligence. One of the hottest …