With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

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 …

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …

Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …

Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical
representations automatically from massive input data, presents a promising tool for …

Gearboxes are widely used in rotating machinery and various industrial applications for
transmission of power and torque. They operate for prolong hours and under different …

Data-driven condition monitoring reduces downtime of wind turbines and increases
reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that …

Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …

The finite-time consensus fault-tolerant control (FTC) tracking problem is studied for the
nonlinear multi-agent systems (MASs) in the nonstrict feedback form. The MASs are subject …