Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

Abstract Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine
learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The …

In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Intelligent fault diagnosis based on machine learning has yielded a wealth of research
results. However, fault diagnosis under small samples is still a challenging problem due to …

Most deep-learning models, especially stacked auto-encoders (SAEs), have been used in
recent years for the diagnosis of faults in rotating machinery. However, very few studies have …

Safe and reliable operation of mechanical equipment depends on timely and accurate fault
diagnosis. When the actual working conditions are complex and variable and the available …