Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …

Graph convolutional networks (GCNs) as the emerging neural networks have shown great
success in Prognostics and Health Management because they can not only extract node …

Noises widely exist in the actual working environment of rotating machinery, which makes it
difficult to extract high-quality fault features. Recently, the superiority of graph neural network …

Graph data-driven machine fault diagnosis methods make success using sufficient data
recently. However, in the actual industry, there are rare failure data in historical data, leading …

Due to the spatially dispersed occurrence of faults and the challenges associated with
sensor installation in axial flow pump equipment, an underwater acoustic signal collection …

Rolling bearings are crucial components in a wide range of rotating machinery, playing a
vital role in maintaining safe and reliable industrial production. Transfer learning techniques …

Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height
recently. However, their satisfactory performance heavily relies on the availability of …

Benefiting from the excellent capability of data processing, deep learning-based methods
have been well applied in fault diagnosis. However, these methods may perform poorly due …

In existing research on rotating machinery diagnosis using graph neural networks (GNNs),
most methods are based on vibration analysis under contact sensor monitoring. However …