Machine learning (ML) based bearing fault detection is an emerging application of Artificial
Intelligence (AI) that has proven its utility in effectively classifying various faults for timely …

Bearing faults are critical in machinery; their early detection is vital to prevent costly
breakdowns and ensure operational safety. This study presents a pioneering take on …

This paper aims to improve the ABC analysis method used for inventory management by
applying the Pythagorean Fuzzy TODIM approach. ABC analysis is one the well-known and …

To address the problems of the difficult processing and internal microstructure disorder of
porous bearing cages, Polyetheretherketone (PEEK) porous self-lubricating bearing cage …

Slope entropy (SlopEn) has been widely applied in fault diagnosis and has exhibited
excellent performance, while SlopEn suffers from the problem of threshold selection. Aiming …

An infrared thermography method is a promising tool for defect detection in rotating
machines, as this approach is a non-intrusive and no-contact kind of approach. Although the …

The fault identification and forecast based on vibration signals is an important approach to
guarantee the safety of equipment. Rolling bearing is one of the most important parts of …

In recent years, nano-filler-based hybrid composites have gained significant attention from
the research community; The nano-filler-based hybrid composites can have potential …

There is a great need to perform all processes and services more efficiently to reduce
energy consumption and material waste. Bearing systems are present in all machines and …

The use of nano-fillers as reinforcement in natural fibers-based hybrid composites has
gained prominence in multiple sectors in recent years because of their virtuous mechanical …