Recently, with the development of industrial informatization and intellectualization, the
development of smart bearings has attracted a lot of significant attention in an attempt to …

Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable
for predictive maintenance of aero-engine under cross-working conditions. Although data …

This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

The development of deep learning algorithms has extensively empowered humanity's task
automatization capacity. However, the huge improvement in the performance of these …

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

Currently, convolutional neural networks (CNNs) have shown great potential in the field of
rotating machinery fault diagnosis. To maximize accuracy, the network architecture of novel …

Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee
safety and reliability of mechanical systems. Due to the variability of machinery working …

Recent intelligent diagnostic algorithms for industrial practice have achieved impressive
results. However, due to safety considerations, complex environments and deployment cost …

The remaining useful life (RUL) of solid-state lithium-ion battery (SSLIB) is a crucial
challenge for their future marketability due to the fact that it guarantees the safety and …

Federated learning is an emerging technology that enables multiple clients to cooperatively
train an intelligent diagnostic model while preserving data privacy. However, federated …