As a promising tool for intelligent diagnosis of rotating machinery with unlabeled data,
transfer learning (TL) has attracted considerable attentions from academia and industry …

Abstract Deep Neural Network (DNN) models have been extensively developed for
intelligent bearing fault diagnosis. The superior performance of DNN-based fault diagnosis …

Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …

Accurate diagnosis of power transformer fault type is critical to maintaining its safe and
stable operation. Existing methods require a large number of labeled data and are …

With society's wide-scale adoption of information technology, significant information about
borrowers is distributed across various parties, information that can be jointly used to …

Leveraging distributed data from various clients to tackle target issues has become a
prominent trend in fault diagnosis. However, the growing concerns about data privacy have …

As fixed-wing unmanned aerial vehicles (FW-UAVs) are used for diverse civil and scientific
missions, failure incidents are on the rise. Recent rapid developments in deep learning (DL) …

The effective robust management of plant requires the implementation of industrial alarm
systems in a very significant capacity. The core objective of alarms is to warn the operator of …

Due to the diverse equipment and uneven load distribution in industrial environments, data
regarding faults are often unbalanced. Moreover, data and models from clients may become …

Training deep learning models on in-home IoT sensory data is commonly used to recognise
human activities. Recently, federated learning systems that use edge devices as clients to …