The scarcity of fault samples has been the bottleneck for the large-scale application of
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …

Knowledge and data dual-driven (KDDD) represents a novel paradigm that leverages the
strengths of data-driven methods in feature representation and knowledge transfer, while …

Deep learning has undergone significant progress for machinery fault diagnosis in the
Industrial Internet of Things; however, it requires a substantial amount of labeled data. The …

Federated learning does well in jointly training multiple deep learning models for fault
diagnosis. The accuracy of local model may affect the effectiveness of federated learning. In …

With the development of Autonomous Marine Vehicles, research on real-time health
monitoring based on data from remote monitoring and shore-based control center has …