With increasing concerns for data privacy and ownership, recent years have witnessed a
paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL) …

Wind turbine (WT) condition monitoring has gained increasing interests in the era of the
Internet of Energy (IoE), and existing monitoring approaches mainly focus on training a …

Data-driven intelligent systems provide a possible solution to condition-based maintenance
of wind turbines without experts' knowledge or mechanism models. However, the accuracy …

The data imbalance problem extensively exists in wind turbine fault diagnosis, resulting in
the compromise between learning attention to majority and minority classes. In this paper, a …

Deep learning-based incipient fault diagnostic techniques have achieved surprisingly well in
wind turbines. Due to component failures, wind turbines must undergo active maintenance …

Abstract Text, 2D, and 3D information are crucial information representations in modern
science and management disciplines. However, complex and irregular 3D data produce …

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet
of Things, manufacturing processes are evolving towards integration, large-scale …

Federated learning has recently emerged as a privacy-preserving distributed machine
learning approach. Federated learning enables collaborative training of multiple clients and …

With the rapid advancements in smart meters, energy internet, and high-performance
computing technologies, deep learning methods are increasingly used for detecting …

Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …