Defining the Residual Resource of Metallurgical Equipment Components Under Limited Data Conditions
Abstract
This study presents the development and validation of a machine learning model for predicting the residual life of bronze bearing liners used in the universal spindle of a 1680 rolling mill. The model is based on the Random Forest ensemble algorithm and implemented in the RStudio environment using real industrial obtained from 36 bearing replacement events. The input feature set includes operating time, replacement frequency, failure probability density, and replacement count, enabling the identification of nonlinear relationships between technological parameters and component degradation. Model performance was evaluated using mean squared error (MSE) and mean absolute error (MAE), achieving an MSE of 0.125 and an MAE of 0.32 days on the test data set, with cross‐validation confirming model stability (MSE = 0.130 ± 0.012). Sensitivity analysis demonstrated robustness to input data variations typical for industrial environments. Comparative analysis with linear regression showed significantly lower predictive accuracy of conventional statistical approaches. The obtained results confirm the feasibility of applying ensemble machine learning methods for reliable residual life prediction under small‐sample industrial conditions. The proposed approach enables data‐driven maintenance planning, reduces the risk of unplanned downtime, and supports the implementation of intelligent condition monitoring systems aligned with Industry 4.0 principles.