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 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 …

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 …

Abstract The Internet of Things (IoT) is crucial in developing next-generation high-speed
railways (HSRs). HSR IoT enables intelligent diagnosis of trains using multi-sensor data …

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

Exploring historical measurement data-driven health monitoring schemes for roller bearings
is a current research hotspot. In engineering practice, the type of fault data obtained is often …

Predicting remaining useful life (RUL) of machinery is of vital importance to prognostics and
health management. Reliable and accurate RUL prediction not only can reduce …