To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

As the core for the lubrication and operation of space ball bearing, the porous non-metallic
cage with oil-impregnated will cause catastrophic results in case of fault. The principal used …

The development of AI has enabled the fault detection of industrial components to be
achieved through the combination with deep learning. A detection method combined with …

In complex working site, bearings used as the important part of machine, could
simultaneously have faults on several positions. Consequently, multi-label learning …

Data-driven models, such as deep learning and transfer learning algorithms, have achieved
leading results in essential tool condition monitoring (TCM) during manufacturing process …

Extraction of weak transients is vital for realizing the early machinery fault diagnosis.
However, a significant challenge lies in an efficient determination of the spectral boundaries …

The accurate determination of the tool-wear state helps workers maximise tool utilisation
while reducing waste. It also ensures the machining quality and improves the machining …

Compound defect is the common damage of equipment. However, measuring compound
defect is difficult because of the complex correlation between multiple single-point defects …

Three frequently encountered problems—a variety of fault types, data with insufficient labels,
and missing fault types—are the common challenges in the early fault diagnosis of space …

Accurate prediction of tool wear status plays a critical role in the digital manufacturing
industry, and its health level directly affects machining quality, production costs, and overall …