Blade tip timing (BTT) is a core technique for investigating the blade vibration of large-scale centrifugal compressors, and identifying the parameters of blade asynchronous vibration is crucial for implementing blade condition monitoring based on the BTT technique. In this study, the multiple signal classification (MUSIC) algorithm and the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm were first applied separately to identify the asynchronous vibration parameters of centrifugal compressor blades, with their advantages and disadvantages discussed. Subsequently, based on the frequency distribution characteristics in the ESPRIT results, the concept of “frequency distribution rate” was proposed. Finally, the results of the MUSIC algorithm were weighted by the frequency distribution rate, and an improved MUSIC algorithm was proposed. This enhanced confidence in the real frequency in the MUSIC algorithm results. Compared with the strain gauge method, the maximum relative error of the improved algorithm is 0.23%. The improved MUSIC algorithm improves the accuracy of parameter identification for blade asynchronous vibration, which holds great significance for the industrial application of the BTT technique.

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.

The ride stability and driving comfort of vehicles are highly dependent on the performance of the damping system. The fatigue life prediction of damping components using rubber as the core damping material has become a research hotspot in the field of vehicle vibration isolation. Taking an automotive engine rubber isolator as the research carrier, this paper jointly carries out finite element simulation analysis and structural component fatigue life tests. A dual-parameter mapping framework is proposed, which integrates maximum strain energy density and cyclic energy dissipation instead of using a single damage indicator. This approach comprehensively accounts for the coupling effect of energy storage and energy dissipation coexisting under actual service conditions. Through uniaxial tensile tests on rubber specimens, combined with finite element simulations and physical model parameters, a quantitative mapping relationship between laboratory specimens and full-scale engine rubber isolators is established. Based on this mapping, the fatigue life curve of the isolator is derived from the specimen-based failure characteristics. Validation tests under two randomly selected operating conditions yield prediction errors of 7.5% and 6.9%, demonstrating that the proposed model can accurately achieve equivalent fatigue life transformation from small specimens to actual components. Unlike conventional direct extrapolation methods, this approach does not require complex multiaxial fatigue tests on the component itself; it only needs simple specimen fatigue data, significantly reducing development costs, while providing a reliable theoretical basis for material selection, fatigue performance optimization, and forward structural design of rubber isolators.