Identifying radial internal clearance from nonlinear rolling-bearing dynamics using a physics-informed approach
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Ambrożkiewicz Bartłomiej, Czarnacki Maciej, Litak Grzegorz, Georgiadis Anthimos |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 61 |
| Numer artykułu: | 133 |
| Strony: | 1 - 30 |
| Impact Factor: | 2,2 |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by the Lublin University of Technology under the ’Investing in Potential’ programme, Grant No. 2/IP/2024/F (Bartłomiej Ambrożkiewicz). |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 24 października 2026 |
| Abstrakty: | angielski |
| Rolling-element bearings exhibit strongly nonlinear dynamic behaviour due to radial internal clearance, intermittent Hertzian contact, eccentricity, damping, and raceway imperfections. This study addresses the inverse identification of radial internal clearance in a nonlinear 2-DOF rolling bearing system governed by intermittent contact. A physics-informed neural formulation is employed to reconstruct the two-dimensional bearing response and simultaneously estimate the unknown clearance by embedding the governing equations of motion directly into the training loss. A SIREN architecture with sinusoidal activation functions is used to represent the oscillatory displacement response and its time derivatives. The framework is assessed using limited and noisy displacement observations generated from 4th-order Runge–Kutta solutions for different clearance values and operating conditions, with noise levels of 1%, 5%, and 10%. The method accurately reconstructs both transient and steady-state responses. The clearance-normalized vector reconstruction error ranges from 0.085% to 0.162%, while the maximum relative error in radial internal clearance identification remains below 0.012%. No systematic deterioration in reconstruction or identification accuracy is observed with increasing noise under the considered conditions. These results establish a physics-informed computational framework for identifying radial internal clearance from nonlinear bearing dynamics. Although the present validation is based on model-generated observations, the formulation provides a basis for subsequent assessment using experimental bearing data. |
