As core vibration-damping components in rail transportation and aerospace, elastic supports are vital to equipment operational safety and maintenance cost control. Addressing the offline cumbersomeness and insufficient accuracy of traditional methods, as well as the poor adaptability of existing models to nonlinear damage and small samples, this study proposes a high-precision life prediction method based on a self-constructed full-life dataset and optimized random forest (RF). A self-developed triaxial vibration test bench was used to conduct accelerated aging tests on rubber–metal composite elastic supports, constructing a unique full-life dataset (412 valid samples) by collecting vibration signals via accelerometers and eddy current sensors. After extracting features like acceleration RMS and natural frequency, core damage-sensitive features were screened through PCA and Pearson correlation coefficients. The RF was optimized with a time-decaying factor and feature and parameter joint optimization to capture temporal degradation and resist overfitting. Experimental results show that the model achieves RMSE = 0.026 and R2 = 0.988, significantly outperforming Gray Prediction, BP Neural Network, and XGBoost. It accurately captures the life evolution law of elastic supports, providing reliable technical support for online life prediction and predictive maintenance.
Zhang et al. (Thu,) studied this question.