20.500.12556/RUL-152638 A data-driven simulation and Gaussian process regression model for hydraulic press condition diagnosis Improving overall performance and increasing operational reliability are currently among the leading research topics in the field of hydraulic systems. In recent years, the use of artificial intelligence-based modeling and design techniques has developed rapidly to account for the nonlinear properties of Gaussian systems and to predict fault reasoning in hydraulic systems. In this study, feature acquisition and selection are proposed to prepare input data for a simulation-based learning approach. In addition, a cause-and-effect analysis is performed by considering various what-if scenarios as external disturbances that affect the response of the hydraulic press. While the objective of the sheet metal bending cycle and a pulley system is to initiate a load on the hydraulic press, an intelligent sensing system is used to observe the behavior of the hydraulic press during the phases of sheet metal bending cycle, i.e., the forming, leveling, and movement. In addition, the Gaussian process regression method is used to build data-driven prediction models with different predictors that contribute significantly to improving predictive accuracy. The condition diagnosis indicates the accurate performance of predictive models observing the coefficient of determination R$^2$ at 0.998 for the bending phase, 0.962 for the leveling phase, and 0.999 for the movement phase. Although the approximation of the simulation model is efficient, it is found that certain features are reasonably well approximated with regard to the forming phases. hydraulic system artificial intelligence Gaussian regression modeling simulation-based learning condition monitoring cause and effect analysis hidravlični sistem umetna inteligenca Gaussovo regresijsko modeliranje spremljanje stanja simulacijsko učenje analiza vzrokov in učinkov true false true Angleški jezik Slovenski jezik Članek v reviji 2023-12-01 13:31:02 2023-12-01 13:31:06 2025-02-11 09:07:26 0000-00-00 00:00:00 2024 0 0 22 str. Vol. 59, art. 102276 Jan. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.22:004.8 1474-0346 10.1016/j.aei.2023.102276 174551043 1-s2.0-S1474034623004044-main.pdf 1-s2.0-S1474034623004044-main.pdf 1 DADC325C4D71CC30CF79D75D705210D6 5ee37df0b3ad604a3b4a64ddc911a9b2cd3814fcbe501285be49f3156ea6355f 26a3f807-c1d2-11ee-9ac8-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=180511 https://www.sciencedirect.com/science/article/pii/S1474034623004044 1 8014b3ea-9045-11ee-b3b7-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=178226 Fakulteta za strojništvo 0 0 0