Team analyzes rolling contact bearing models with a look toward a more robust modeling framework

Rolling contact bearings are an important part of mechanical systems that reduce friction between moving parts. Scientists need to be able to accurately model these bearings so they can predict their performance, durability, and failure mechanisms. Even though significant advancements have been made, there is still a need for a unified framework to evaluate and compare the existing modeling approaches. A research team has studied the most significant rolling contact bearing models, identifying key trends and advancements. Their findings reveal a significant variability in the modeling approaches used and highlight the lack of standardized methodology for modeling these complex systems.

Their work is published in the journal Friction on February 5, 2026.

Bearings play a critical role in a wide variety of mechanical systems, especially in rotary machinery, since they enable a relative motion between moving and stationary parts, that allows a smooth and efficient service. While they are many different types of bearings used in machinery, the research team focused their study on rolling contact bearings. These particular bearings require high versatility and are used in applications ranging from household appliances to aerospace systems.

Because bearings are one of the most used machine elements, they also represent one of the main sources of failures in rotary machinery. Some studies show that approximately half of motor breakdowns can be attributed to bearing malfunctions. When bearings malfunction, typically the machinery must be totally stopped to inspect and determine the cause of the failure and then to change the component. These maintenance activities cost a company time and money. Most of these breakdowns could be avoided through the prompt identification of any associated defects.

Traditional maintenance methods focus on replacing the bearing at certain time intervals. These traditional methods do not consider inappropriate operation situations, such as improper assembling or poor lubrication. Nor do these methods regard the location and accessibility of the equipment, for example offshore turbines, which can make replacement procedures more difficult and expensive. Predictive maintenance solutions can be useful tools for overcoming these challenges in the traditional maintenance methods. With predictive maintenance, engineers can accurately evaluate the condition of the bearing without using the periodic replacement procedures.

“The utilization of computational modeling techniques for predictive maintenance purposes in the context of rolling contact bearings has been demonstrated to be a promising avenue of research over the past decades,” said Raúl Gismeros Moreno, a professor at the Universidad Carlos III de Madrid

The research team analyzed more than 50 relevant rolling contact bearing models, preparing comprehensive summaries and comparative tables. “This work provides a valuable resource for researchers and engineers, guiding future developments toward more robust and comprehensive modelling frameworks,” said Moreno. The team conducted their analysis from March to October 2023, using the electronic databases Web of Science and Scopus. The works they considered for their analysis were published in the English language; however, they placed no restrictions on the publication year of the works.

The team’s work reveals the importance of studying bearing systems, since they are a major contributor to machinery breakdowns and, therefore, a critical focus of research. As intelligent digital technologies are increasingly added to manufacturing process, these models must be able to reduce maintenance costs and downtimes. So the need for accurate, yet computationally efficient models, has grown.

From their analysis of the models, the team concluded that there is a lack of a standard procedure in the approaches used. There is no optimal solution modeling these complex systems. “Future research should define different, concrete strategies for the modeling, with different degrees of complexity depending on the application of the bearing system, for example, if the analysis is focused on the bearing itself or if it is part of a larger, complex mechanical system,” said Moreno.

The research team includes Raúl Gismeros Moreno, Eduardo Corral Abad, and Jesús Meneses Alonso from the Universidad Carlos III de Madrid, Spain; and Filipe Marques and Paulo Flores from the Universidade do Minho and LABBELS–Associate Laboratory, Rua da Universidade, Portugal.

This research is funded by the European Union through its program NextGenerationEU/PRTR and the Portuguese Foundation for Science and Technology.

DOI Link:

https://doi.org/10.26599/FRICT.2025.9441147